Download Computer-Aided Drug Design of Bioactive Natural Products (PDF

Send Orders for Reprints to [email protected]
Current Topics in Medicinal Chemistry, 2015, 15, 1780-1800
1780
Computer-Aided Drug Design of Bioactive Natural Products
Veda Prachayasittikul1,3, Apilak Worachartcheewan1,2, Watshara Shoombuatong1,
Napat Songtawee1, Saw Simeon1, Virapong Prachayasittikul3 and Chanin Nantasenamat1,3,*
1
Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand; 2Department of Clinical Chemistry, Faculty of Medical Technology,
Mahidol University, Bangkok 10700, Thailand; 3Department of Clinical Microbiology and Applied
Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
Abstract: Natural products have been an integral part of sustaining civilizations because of their medicinal properties. Past discoveries of bioactive natural products have relied on serendipity, and these
compounds serve as inspiration for the generation of analogs with desired physicochemical properties.
Bioactive natural products with therapeutic potential are abundantly available in nature and some of
them are beyond exploration by conventional methods. The effectiveness of computational approaches
as versatile tools for facilitating drug discovery and development has been recognized for decades, without exception, in
the case of natural products. In the post-genomic era, scientists are bombarded with data produced by advanced technologies. Thus, rendering these data into knowledge that is interpretable and meaningful becomes an essential issue. In this regard, computational approaches utilize the existing data to generate knowledge that provides valuable understanding for
addressing current problems and guiding the further research and development of new natural-derived drugs. Furthermore,
several medicinal plants have been continuously used in many traditional medicine systems since antiquity throughout the
world, and their mechanisms have not yet been elucidated. Therefore, the utilization of computational approaches and advanced synthetic techniques would yield great benefit to improving the world’s health population and well-being.
Keywords: Natural products, Biological activity, Data mining, Drug discovery, Computer-aided drug design.
INTRODUCTION
Far-reaching impacts of natural products on human being
have been noted for centuries in the realms of home remedies and medicines. Historical evidence of the first natural
products was revealed through paleoanthropological studies
in which pollen deposits were found in the grave of Shanidar
in present-day Iraq, which is estimated to date back to more
than 60,000 years ago [1]. The importance of natural products to civilizations can be attributed to their diverse pharmacological properties. Medical records on the use of natural
products as therapeutics have been documented across regions. Furthermore, a clay tablet depicting information regarding medicinal extracts (i.e., resins, oils and juices from
approximately 1,000 plants) was discovered in Mesopotamia
and dates back to 2600 B.C. [2]. The Ebers Papyrus, an
Egyptian medical text contained information on plant-based
remedies for various diseases [3]. The first known Chinese
text on this subject was called Wu Shi Er Bing Fang (containing 52 prescriptions), followed by Shennong Herbal
(containing 365 drugs) and Tang Herbal (containing 850
drugs) [4]. As for western countries, historical evidence for
the use of natural products was identified in monasteries of
England, Ireland, Germany and France during the dark and
middle ages [4]. Furthermore, it should not be overlooked
*Address correspondence to this author at the Center of Data Mining and
Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand; Tel: 66-2-441-4371; Fax: 66-2-441-4380;
E-mail: [email protected]
1873-5294/15 $58.00+.00
that Avicenna, the Persian pharmacist, made significant contributions to the field of pharmacy through his work “Canon
Medicinae” [4].
Historical records identified medicinal plants, fungi and
algae as rich sources of bioactive natural products [5]. The
use of medicinal plants originated with respect to the human
instinct for survival, i.e., searching for food and seeking to
avoid death [6]. Native Americans, used ashes of the plant
genus Salvia to aid childbirth and protect infants from respiratory diseases [7]. The ancient Europeans used Parmelia
omphalodes extracts to cure burns and cuts due to its antiinflammatory properties [8]. Fungi have been used as food
(mushrooms), raw materials for perfumes and cosmetics, and
ingredients for preparing alcohol and medicine since the early Chinese and Egyptian civilizations [9]. Fungi in the Anthozoans species, i.e., Chondrus crispus, were widely used
for the treatment of chest infections [10]. Parmelia omphalodes (Linnaeus) Acharius were widely used in the British
Isles as a dye and in Ireland as an anti-inflammatory agent to
cure burns and cuts [11]. Among algae, the juice of the red
alga Porphyra umbilicalis (Linnaeus) Kützing has been noted for its anticancer properties, particularly with respect to
breast cancer [12].
The importance of natural products in medicine has been
indicated by the continual use of classical natural products.
One of the classic examples of a natural product is Papaver
somniferum, the opium poppy, which contains naturally occurring alkaloids as bioactive compounds [13]. From the
© 2015 Bentham Science Publishers
Computer-Aided Drug Design of Bioactive Natural Products
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
Egyptian to Chinese civilizations, opium was cultivated and
used for several purposes. Ancient physicians used it as an
anesthetic agent to perform medical surgery [14]. Likewise,
they were used as painkillers during the American Civil War.
In addition, they were used as recreational drugs in ancient
China.
The Chinese and Indians are considered to be the pioneers of herbal medicine, and their formulae have had great
impacts on the traditional medicine of many countries
worldwide [15]. The knowledge of the Chinese and Indians
has been exchanged for a long time through the silk road
[16]. Ayurveda is an Indian traditional medicine that defines
the body in terms of three main constitutions (dosha), and
the dynamic equilibrium of these dosha is essential for normal bodily function [17]. In contrast, the disturbance of these
dosha is believed to be the root causes of diseases [18].
Similarly, Traditional Chinese Medicine (TCM) defines yin,
yang and qi as the three main biological forces in the human
body. The balanced equilibrium of yin and yang is essential
for being healthy, and qi is required as the energy that circulates and nourishes the entire body [19, 20]. Traditional Chinese medicine is considered to be the prototype of Japanese
traditional medicine (kampo medicine) [21] and Korean traditional medicine or Sasang constitution medicine (SCM)
[22], to which the original formulae have been adapted. The
Chinese and Ayurvedic traditional medicine systems have
had great impacts on traditional medicine in Asian countries,
including Thailand. The history of utilizing natural products
for medicinal purposes has been noted since the Ayutthaya
period (1350–1767 A.D.) [23].
Both Ayurveda and TCM are herbal medicine systems in
which herb formulae that contain various medicinal herbs are
prescribed to provide synergistic effects and reduce adverse
effects [24]. Despite having distinct formulae, the traditional
medicines of India and China are based on the same belief
that an individual’s physical constitution plays a major role
in susceptibility to diseases and its response to treatment
[25]. The prescribed formulae can be adjusted according to
the patient’s condition [24]. A similar basis of different body
constitutions that lead to differential responses to herbs is
also implied SCM [26].
What biomolecule can
be a target?
Which compounds can
bind target ?
↑ Potency
↑ Drug-likeness
↓Toxicity
The unique characteristics of these traditional medicines
are in agreement with modern individualized medicine [27].
Furthermore, recent studies have revealed the relationships
between traditional medicine systems (i.e., Ayurveda [28,
29], Chinese [30, 31], Japanese [32, 33] and Korean [34-36])
and genomic differences of individuals [27], which renders
these systems thought-provoking alternative personalized
treatment strategies in the post-genomic era [27].
The great importance of natural products in human being
has been documented. Approximately 11% of drugs in the
WHO’s essential medicines list are exclusively derived from
plants, and 25% of the drugs prescribed worldwide are plantderived products [37]. Most of the African and Asian populations rely on traditional medicine for their primary
healthcare [38] because of limited access to healthcare facilities and healthcare professionals [39], affordability and belief of safety [40]. In addition, the ancient use of natural
products has formed the basis of later clinical, pharmacological and chemical studies [5], which can be identified from
the discovery and development of many currently used
drugs, e.g., aspirin, morphine, digitoxin, quinine and pilocarpine [41].
Currently, the botanical statuses of countries differ because of distinct features of advancement in science and
technology, regulations within the country, culture and society [42]. In the Europe Union (EU) and the United States
of America (USA), herbal extracts are used as active compositions in herbal medicinal products, dietary supplements (in
the USA) and food supplements (in the EU). In Asian countries, natural products from plants are widely used as drugs
for therapeutic purposes in traditional medicine and are used
as health foods for the prevention of diseases and promotion
of good health [42].
DRUG DISCOVERY AND DEVELOPMENT
Drug discovery and development is a complex process
that requires expertise from multidisciplinary fields. It consists of many time consuming processes, from target identification to clinical trials, that require substantial financial
efforts (Fig. 1) [43]. According to the complexity of drug
development processes, bioinformatics and computational
Target
identification
Data mining tools
Molecular docking
Hit
identification
Virtual screening
Structural-based :
•  Molecular docking
Ligand-based :
•  Pharmacophore
•  Machine learning
•  Similarity
Hit-to-lead
1781
QSAR
QSPR
Lead
optimization
Pre-clinical
trials
Clinical trials
Phase I, II, III
Drug
Approval
Market
Fig. (1). Conceptual framework of drug discovery and development and the roles of computational approaches. (Hits = compounds that can
bind to a target, Leads = hits with preferable potency, QSAR = quantitative structure-activity relationships, QSPR = quantitative structureproperties relationships).
1782
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
approaches have become versatile tools for facilitating and
accelerating drug design and development [44, 45]. The conceptual framework of drug discovery and development and
the roles of computational approaches in each step are illustrated in (Fig. 1).
Target identification is the process in which drug targets
are identified [43] by databases that include experimental
results. [43] The associated data mining tools are useful for
creating databases, and molecular docking is capable of identifying potential targets by docking drugs to large libraries of
proteins [43]. Hits are defined as groups of compounds that
exhibit desired activity in the screening process [43]. The
process of hit identification can be performed using high
throughput screening (HTS) and virtual screening. HTS is
performed by screening an entire library of compounds
against the target by automation; however, secondary assays
are required for confirmation [46]. Virtual screening is an
effective means of searching for potential compounds by
using computational approaches. One widely used computational method in this process is molecular docking. The crystal structure of the target protein is required to simulate binding in silico against large libraries of compounds. Active
compounds with good binding affinity to the target, represented by a docking score [43], are identified as hits and will
be further developed [47]. Hits are subsequently optimized
to obtain improved potency and pharmacokinetic properties
and reduced toxicity [43]. The optimization is performed by
structural modification of compounds, where medical chemistry and computational approaches play essential interactive
roles [48]. Quantitative structure-activity relationships
(QSAR) and quantitative structure-properties relationships
(QSPR) are computational methods for correlating the chemical structures of the compounds with their activity
/properties. Understanding these relationships is useful for
structural modification by medicinal chemists in seeking for
potential compounds [43, 48]. In addition, molecular modeling and molecular docking can be used for the discovery of
new binding sites on target proteins [43].
PRIVILEGED STRUCTURES
The similarity principle has been widely applied in drug
design on the basis that structurally related compounds possessing similar chemical structures may elicit similar biological activities [49]. In addition, the importance of most
common molecular fragments or privileged structures has
been noted by Evans et al. in 1988 [50]. Privileged structures
are defined as molecular substructures that are capable of
binding to a diverse array of receptors, and the modification
of these substructures can provide an alternative approach to
the discovery of novel receptor agonists and antagonists
[50]. It also has been suggested that privileged structures
provide affinity towards binding with receptors, whereas the
rest of the molecule defines the selectivity of a potential
compound [51]. Privileged structures have been successfully
used as core structures for the synthesis of novel biologically
active compounds [52-54] and as being a starting point for
the synthesis of libraries [55]. The importance of privileged
structures in drug design and discovery renders computational approaches a powerful tool to address the search for
novel privileged structures. It is widely known that natural
products are rich sources of bioactive compounds. Recently,
Prachayasittikul et al.
diverse types of privileged structures have been identified
from natural products, e.g., indole, quinolone, isoquinoline,
purine, quinoxaline, quinazolinone, tetrahydroquinoline,
tetrahydroisoquinoline, benzoxazole, benzofuran, 3,3dimethylbenzopyran, chromone, coumarin, carbohydrate,
steroid and prostanoic acid [55].
DRUG-LIKE PROPERTIES
Drug likeness is essential for effective drugs because active compounds become useless if they are not capable of
behaving like drugs in clinical situations. Drug likeness is
expressed by drug-like properties that are indicated by
Lipinski’s rule of five [44, 56]. Lipinski’s rule suggests that
drug-like compounds are molecules with molecular weights
(MW) < 500 Da, calculated octanol/water partition coefficients (clogP) < 5, a number of hydrogen-bond donors < 5
and a number of hydrogen-bond acceptors < 10 [56]. However, these rules are used as guidelines rather than as absolute cut-offs for determining drug-like properties [44]. Recently, the importance of other physicochemical and structural properties influencing drug-like properties has been
suggested in terms of property-based design [57]. The basis
of property-based design is that molecules with similar
chemical structures are expected to possess similar pharmacokinetic properties [57].
The pharmacokinetic profiles of drugs, i.e., absorption
(A), distribution (D), metabolism (M), excretion (E) and
toxicity (T), are essential for determining whether such bioactive compounds could be used as safe and efficient oral
drugs [43], and they are considered to be crucial factors for
decision-making in further development of the investigated
compounds [58]. All of these ADMET properties indicate
the drug likeness of compounds and notably affect efficacy,
toxicity and drug-drug interactions [44]. For decades, many
drugs have failed and been withdrawn in the late stages of
drug development, causing considerable financial lost [43,
59]. The two main reasons that lead to the clinical failures of
drugs are poor ADME properties [44] and severe toxicities
(T) [43, 59]. Hence, considerable attention has been paid to
the evaluation of the pharmacokinetic (ADME) properties
and toxicity (T) of investigated compounds in the early stages of drug development to reduce the risk of failures and,
therefore, save time and cost [60, 61]. In this regard, many
computational approaches have been employed for the prediction of ADMET properties [62-67].
COMPUTATIONAL TOOLS
Databases
In recent years, we have witnessed the introduction of a
wide range of databases to aid drug discovery efforts, and
these can be broadly classified into two groups: bioactivity
databases and target databases.
Bioactivity databases are valuable tools for identifying
hit chemical compounds. For example, the ChemNavigator
database (http://www.chemnavigator.com/) is a comprehensive database because it contains over 91.5 million druggable
compounds, although post-curation is needed before performing docking studies and/or quantitative structureactivity relationship (QSAR) studies [68]. ZINC is a dock-
Computer-Aided Drug Design of Bioactive Natural Products
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
ing-studies friendly database (http://zinc.docking.org) because 3D formatted and purchasable 35 million drug-like
compounds are deposited [69]. The ChEMBL database
(https://www.ebi.ac.uk/chembl/) contains over 1 million
compounds with information on their binding affinities,
functional assays, bioactivity measurements and ADMET
properties [70]. Pubchem is a freely accessible repository
that contains more than 63 million compounds and provides
diverse bioactivity results for approximately 45 million. One
of the features that makes Pubchem an attractive tool for in
silico drug design is the PubChem Download Service [71].
Binding DB is a public and openly accessible database that
has approximately 20,000 binding affinities of small compounds that have been experimentally tested with known 3D
structural available protein targets.
Target databases are important for identifying druggable
proteins that are involved in pathogenesis. For instance, the
tropical disease pathogens target database (http://tdrtargets.
org) contains information on protein structures, functional
genomics and biochemical pathways to aid the in silico identification of protein targets [72]. The potential drug target
database (http://www.dddc.ac.cn/pdtd/) contains over 1,100
3D druggable protein structures ranging from enzymes to
lipid binding proteins [73]. The Therapeutic Target Database
(http://xin.cz3.nus.edu.sg/group/ttd/ ttd.asp) contains over
2,360 targets with information on 3D structures, diseases,
binding properties and functional properties [74]. The Pro-
1783
tein Data Bank (PDB) contains all known crystallized 3D
protein structures, conveniently providing new information
(structural information that is not provided in the sequence
database, e.g., GeneBank) and tremendously aiding in silico
drug design by allowing researchers to identify novel potential drug targets and to perform docking studies [75].
Chemical Space of Natural Products
Chemical space is the total possible number of descriptors from chemical compounds. Similar to the spatial
extent of space the universe, these descriptors are infinite in
number. Despite the advancement in the synthesis of organic
compounds and the characterization of nature products, only
a small fraction of compounds have been synthesized and
used. Thus, by exploring the origin of chemical space in living organisms, new strategies to combat diseases will
emerge. Visualization of the chemical space of natural products obtained from 12 natural product databases available
from the ZINC database is shown in (Fig. 2) by means of a
PCA scores plot.
Chemical space analyses of FDA approved drugs were
performed to explore the properties and characteristics of
drug-like chemical compounds. For example, Vieth et al.
[76] studied fragment analysis of 1,082 FDA approved drugs
and 1,729 marketed drugs. The results showed that the halogen contents of marketed drugs are identical, and the molec-
1.5
1.0
Database
AfroDb
AnalyticCon
HIT
0.5
IBScreen
PC2
Indofine
NPACT
0.0
Nubbe
Princeton
Specs
−0.5
TCM
Tongju
UEFS
−1.0
−1.5
−1.5
−1.0
−0.5
0.0
0.5
1.0
1.5
PC1
Fig. (2). PCA plot of compounds from 12 databases obtained from the ZINC database. Random selection of 100 compounds from each of the
12 databases was carried out followed by representing each compound by the ECFP substructure fingerprint. Finally, PCA was computed in
R using the prcomp function from the stats package and the resulting plot is visualized using the ggplot2 package. Acronyms and full names
of the 12 databases are provided hereafter (AfroDb: African natural products, AnalyticCon: AnalytiCon discovery natural products, HIT:
Herbal Ingredients Targets, IBScreen: IBScreen natural products, Indofine: Indofine natural products, NPACT: Naturally occurring plant
based anticancerous compound-activity-target database, Nubbe: Nuclei of Bioassays, Biosynthesis and Ecophysiology of Natural Products,
Princeton: Princeton natural products, Specs: Specs natural products, TCM: Traditional Chinese Medicine Database, Tongju: Tongji University herbal ingredients in vivo metabolism, UEFS: Universidade Estadual De Feira De Santana natural products).
1784
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
Prachayasittikul et al.
ular weights of FDA approved drugs are lower than 500.
These results were consistent with Lipinski's rule of five,
which claims that drugs should possess a MW smaller than
500 to have good bio-absorption and bioavailability. Chemical space analyses were performed on natural products as
well. For instance, Ganesan [77] used 24 unique natural
products to understand the associated chemical space. Of all
the 24 natural products, half of them obey Lipinski’s rule of
five, whereas the other half disobey the rule. A closer examination of the physicochemical properties of these 24 natural
products revealed that almost all of them obey the log P rule,
such that their values are smaller than 5.
they were clearly different. For example, for the structural
properties of bioactive natural products, the molecular
weights, the number of rings, the number of carbon atoms
and the number of oxygen atoms, in particular, were higher
than those of non-bioactive natural products [83]. In contrast,
the results showed that most of the bioactive natural products
exhibited drug likeness despite having increased numbers of
hydrogen bonds donors and acceptors. This result suggested
that natural products have desirable properties in drug discovery and development because compounds that obey the
rule-of-five are orally active and very specific in binding to
their targets [83].
Koch et al. [78] explored the chemical space of natural
products by classifying their chemical scaffold, which allowed the identification of 11 novel ß-hydroxysteroid dehydrogenase type 1 inhibitors. Reayi and Arya [79] stressed
that the chemical space of natural products can be populated
by diversity-oriented synthesis (DOS), a strategy in chemical
synthesis to quickly create a library of compounds, which
will aid in the deorphanization of druggable protein targets.
Josefin et al. [80] utilized ChemGPS-NP to explore the
chemical space of natural products from several databases
and found that 40,348 compounds from the Dictionary of
Nature Products Database passed the Lipinski’s rule of five.
Osada and Hertweck [81] claimed that the chemical space of
natural products is populated naturally by gene clustering,
where gene natural product synthesizer enzymes are altered
to increase their chemical space. Lachance et al. [82]
claimed that the bioactivity of the chemical space of natural
products can be analyzed, charted and navigated to identify
relevant substituents to aid modern chemical synthesis in
drug discovery and development.
To compare the drug-likeness and BNC-likeness models,
a data set of 59,000 drugs from the World Drug Index (WDI)
were randomly chosen and screened to obtain 3,930 compounds, of which 1,965 were bioactive and 1,965 were nonbioactive. Molecular descriptors were extracted for each
compound to develop a drug-likeness model using SVM as
the learning technique. The performance of the drug-likeness
model decreased when the natural product data set was used,
and the opposite phenomenon was observed for the BNClikeness model [83]. These two models may have differed
because they rely on different properties of synthesized
drugs and natural products. A closer look at the key descriptors of these two models revealed by the RuleSet algorithm, an algorithm that is based on a decision tree algorithm, indicated only a few important descriptors to perform
the classification. In the development of the BNC-likeness
predictive model, 180 descriptors were used, whereas 328
descriptors were used as inputs to construct the drug-likeness
model. There were significant differences when the distributions of the 180 and 328 descriptors were plotted. To confirm these differences, 1,647 descriptors were extracted from
Dragon based on molecular descriptors for each compound
and were split with the k-means clustering approach. The
descriptors were clustered into 50 groups based on their
Pearson’s correlation coefficients. The important descriptors
for both models (i.e., drug-likeness and NBC-likeness) were
significantly different because the descriptors in clusters 35,
33, 28, and 36 were mainly used to build the BNC-likeness
prediction model, and they were rarely used to create druglikeness models. In contrast, clusters 19, 7, and 18 were
largely used to build drug-likeness models and were rarely
used to make BNC-likeness models [83].
Analogous to the Linpiski’s rule of five (drug-likeness),
Zhou et al. [83] used structure-activity relationships to explore the chemical space of natural products to define “bioactive natural compound-likeness” (BNC-likeness). Structural properties were compared between bioactive and nonbioactive natural products and between the drug-likeness and
BNC-likeness models. A dataset of 1,580 natural products
was obtained from a total of 7,549 natural product ingredients from the Ethnobotanical Database and Dr. Duke’s Phytochemical Database. Of 1,580 natural products, 790 were
bioactive whereas 790 were not, providing a balanced dataset. SVM with radial basis function kernels was used to
perform bioactive natural compound-likeness models, using
1,580 compounds with bioactivity as the training set. The
performance of the models was tested with an independent
external data set that included 81 bioactive natural products
and 81 non-bioactive natural products from widely used medicinal herbs. The prediction results demonstrated that 75
bioactive compounds were successfully classified, suggesting that the models are robust and do not have the problem
of overfitting. Overfitting is one of the problems in machine
learning and occurs when noise data are incorporated as independent variables to develop highly predictive models.
Although these models work very well with internal datasets,
their performance is very low when a new class of data or a
test set is applied [83].
A closer examination of the structural properties between
bioactive and non-bioactive natural products showed that
Natural Products as Sources of Inspiration for New
Drugs
Small molecules and secondary metabolites have been
economically designed and synthesized by nature for the
benefit of evolution; in other words, they have been evolutionarily selected [84]. Regarding the power of evolution,
natural products contain diverse types of biologically relevant privileged structures that have saved millions of lives,
which renders them a continuous source of inspiration for
the discovery of new drugs [85]. These naturally occurring
ligands serve as excellent structural starting points for exploring biologically relevant chemical space [86]. Therefore,
the identification of natural products that are capable of
modulating protein functions in pathogenesis-related pathways is the heart of drug discovery and development [78].
Until now, distinct natural products have been chemically
Computer-Aided Drug Design of Bioactive Natural Products
modified and driven to become Food and Drug Administration (FDA) approved drugs [77]. From 1981 to 2010, natural
products and their derivatives accounted for 74.8% of all
candidate drugs approved by the FDA [87]. Good examples
of natural product-inspired drugs are carfilzomib, omacetaxine mepesuccinate and mitoxantrone.
Carfilzomib is a natural-linked compound derived from a
naturally occurring bacterial proteasome inhibitor, epoxomicin. Carfilzomib was first synthesized in 1992 by Hanada
et al. [88]. However, the mechanism of action of this compound was unknown. In the late 1990s, carfilzomib was
structurally modified by Crews’ lab from Yale University to
obtain a derivative that was structurally similar to the parent
compound, epoxomicin [89, 90]. Several research groups put
forward great effort to structurally modify carfilzomib.
Eventually, a derivative of carfilzomib was obtained by Proteolix and Onyx and was approved by FDA in 2012 for the
treatment of multiple myeloma [91].
Homoharringtonine is a bioactive cephalotaxine alkaloid
isolated from the extract of evergreen trees. In 1976, homoharringtonine was clinically observed for its anticancer potential against acute leukemia [92]. Since then, this natural
compound has been examined across several organizations
and companies. Finally, the ester derivative of homoharringtonine was approved by the FDA in 2012 for the treatment of
chronic myeloid leukemia under the name omacetaxine
mepesuccinate [91].
Mitoxantrone is an anticancer agent derived from the
natural product pharmacophore. Mitoxantrone is a doxorubicin analog that was designed to minimize cardiotoxicity of
its parent compound [93]. Mitoxantrone has been approved
by the FDA for the treatment of many cancers, including
acute leukemia, breast cancer and lymphomas [94]. In addition, it was approved by the European Medicine Agency
(EMEA) of the European Union (EU) in September 2012 for
the treatment of B cell lymphomas [91]. At present, there are
applications of this drug before the FDA for approval for the
treatment of non-Hodgkin’s lymphoma [91].
Finally, the commercial success of these natural-derived
drugs clearly demonstrates that natural products provide
great sources of biologically relevant privileged structures
that are useful as structural starting points for the screening,
design and development of novel potential drugs.
Synthesis of Natural Products
Natural products are in high demand owing to their exceptional range of bioactivities. Some natural products are
limited or inaccessible in nature. Organic synthesis often
solves this problem by supplying these scarce compounds
and enabling the conversion of bioactive natural compounds
into more drug-like derivatives [95]. It is well known that the
chemical structures of the majority of natural products are
complex, which renders their total synthesis a difficult task
[95]. Therefore, novel organic synthetic approaches have
been developed in an attempt to yield potential compounds
with medicinal value [96]. Principally, structural modifications of the natural product core structures are performed to
improve selectivity and potency, to provide additional properties [97], and to facilitate their synthesis [95]. Furthermore,
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
1785
some novel synthetic strategies have been developed to increase structural diversity, in other words, to expand the
chemical space of investigated compounds [84, 98]. Examples of organic synthesis methods are given below.
Semi-synthesis is performed by the chemical modification of natural products to improve potency, selectivity and
other properties [97]. This method has been historically used
to yield a number of therapeutic compounds or compounds
with significant impacts on mankind. A notable example of
this approach is heroin, which is derived from the acetylation
of morphine [99].
Fragment exchange is a complementary approach that replaces chemical fragments of natural products with synthetically derived fragments [97]. Statin and its derivatives, i.e.,
mevastatin, lovastatin, simvastatin and atorvastatin, are
drugs that lower the concentration of lipids. These compounds have been developed from naturally occurring statin
based on the semi-synthesis and fragment exchange methods
[100].
Diversity-oriented synthesis (DOS) is an effective tool to
achieve a library of structurally diverse compounds with
desirable biological properties [101, 102]. Structural diversity is one of the key strategies for expanding the investigated
chemical space and thereby increasing the rate of finding
potential hits [98]. Conceptually, natural products are used as
starting scaffolds to generate compound libraries by various
organic synthesis methods [103], in which novel molecules
are generated in short reaction sequences (not more than 4 or
5 steps) [104]. Examples of natural products used as starting
scaffolds are gibberellic acid (a plant hormone), adrenosterone (steroid hormone) and quinine (isolated compound
from the bark of the cinchona tree) [103].
Function-oriented synthesis (FOS) is an effective strategy
for producing therapeutic lead compounds in a stepeconomical fashion [95] such that small molecules are generated with less structural complexity and with preferable
properties [95]. The principle of FOS is based on the fact
that only a portion (substructure) of a compound is responsible for its biological activities, and these crucial moieties can
be modified to facilitate synthesis, enhance desirable biological activities and improve drug-like properties [95]. It
should be noted that natural products are most likely bind to
multiple targets [84], and they are not designed for human
therapeutic use [95]. These characteristics lead to undesired
side effects and inferior pharmacokinetic properties [95].
The benefits of FOS have been noted to address these problems by reducing undesired side effects, enhancing desired
biological activities and improving pharmacokinetic properties [95]. FOS has been applied for the development of many
natural compounds, such as bryostatin [105], halichondrin B
[106], statin [107], dynemicin [108] and laulimalide [95].
One of the challenges in drug discovery and development
is the identification of biologically relevant areas that are
located inside an investigated chemical space [109]. Biology-oriented synthesis (BIOS) is based on the structural analysis of small molecules and target proteins, where biological
relevance is a prime criterion for the selection of starting
scaffolds for the synthesis of biologically active compound
collections [84]. Briefly, natural product scaffolds are ana-
1786
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
lyzed and classified according to their core structures, and
protein targets are clustered by their similarity. Consequently, scaffold collections and protein clusters are matched by
biological pre-validation [84] to provide a starting point for
the subsequent synthesis of small molecules enriched with
biological activity [86]. In this regard, computational approaches, i.e., chemoinformatics and bioinformatics, are necessary [86]. It should be noted that BIOS only provides a
starting point for discovery, and the continuous development
of practical synthetic methods, i.e., one-pot sequences, cascade and domino reactions, are essential as a final step to
obtain biologically active, naturally derived compound collections [86].
Until now, many synthetic strategies have been reported
for the synthesis of natural product analogs, including the
solid-phase technique [110, 111], solution-phase technique
[96, 111], polymer-immobilized scavenger reagents [112,
113], direct sorting [114], combinatorial biosynthesis [115118], total synthesis using gold catalysis [119] and biologyoriented synthesis [84, 120, 121].
Quantitative Structure-Activity/Property Relationship
(QSAR/QSPR)
Quantitative structure-activity/property relationships
(QSAR/QSPR) describe mathematical and statistical relationships between molecular descriptors of compounds (X)
and their biological activities/properties (Y). Hansch et al.
first demonstrated the use of mathematical and statistical
approaches for constructing a QSAR/QSPR model [122,
123]. Over the last several decades, the QSAR/QSPR model
has been used to effectively reduce time-consuming, laborious and expensive process in innovation drug research [124126], and it has also performed well for the prediction of
physicochemical and biological properties [127-135]. Thus,
it is desirable to develop an efficient and reliable
QSAR/QSPR model to improve the drug discovery process.
The development of a QSAR/QSPR model is essentially
comprised of five major steps: i) calculating the molecular
descriptors; ii) selecting relevant and informative molecular
descriptors; iii) dividing the data into training/internal and
testing/external sets; iv) establishing the QSAR/QSPR model
using the training set; and v) validating the QSAR/QSPR
model.
Calculating the Molecular Descriptors
The chemical structure of a compound can be represented
as a set of numerical values, called molecular descriptors
[136]. First, chemical structures are drawn, geometrically
optimized and calculated to obtain descriptor values. Typically, many types of descriptors, i.e., physicochemical properties, molecular properties and molecular fingerprints, can
be extracted from the chemical structure of natural products
using computer software [137]. Although several thousand
descriptors can be obtained from conventional software
packages, those descriptors may not be informative or useful
for predicting the bioactivity of compounds of interest. Thus,
feature selection via machine learning algorithms is essential
to select a set of informative descriptors prior to the construction of QSAR/QSPR models [125]. Bioactivities are
considered to be the effects of the natural products on the
living organisms, which can be either beneficial or harmful,
Prachayasittikul et al.
depending on their structural composition and concentration.
Accurate and precise bioactivity data are essential for the
successful construction of QSAR/QSPR models. Therefore,
multiple rounds of activity assays should be performed to
obtain accurate and precise bioactivities. Recently, QSAR
models were successfully constructed using several bioactivities, such as minimum inhibition, toxicity, solubility, sorption, absorption, bioconcentration, permeability, metabolism,
clearance and binding affinity [125]. Initially, the chemical
structures of the natural products can be collected from public databases, commercial repositories and the literature.
Chemical structures are drawn, geometrically optimized and
subjected to descriptor calculations [125]. Many types of
descriptors (e.g., constitutional, topological, geometric, electrostatic, fingerprints, steric, quantum chemical descriptor)
can be obtained independently from the software that is used
to perform the descriptor calculation. The physicochemical
properties, quantum chemical properties and molecular fingerprint properties of the natural products can be extracted as
a set of descriptor values by free and/or commercial software
[138-140]. There are openly available descriptor calculators
that permit descriptor extractions for the user. For example,
the free online E-Dragon molecular descriptors calculator
(http://www.vcclab.org/lab/edragon/start.html) allows users
to extract 1,600 molecular descriptors, where SDF (MDL) or
MOL2 (Sybl) input files of the 3D structures (with added
hydrogen atoms) are used as inputs to extract the descriptors
[141]. Another example of a free descriptor calculator is
Jcompoundmapper (http://jcompoundmapper.source forge.
net/); users can download the java client for this application,
called JCMapperCLI.jar, and conveniently calculate molecular fingerprint descriptors using the Command Prompt script
[142]. In addition, molecular structures that represented as
simplified molecular-input line-entry system (SMILES) format together with endpoint or their biological and chemical
properties can use for development of QSPR/QSAR models
by Monte Carlo method in CORrelation And Logic (CORAL) software (http://www.insilico.eu/ coral) [134].
Selecting Relevant and Informative Molecular Descriptors
Many QSAR/QSPR models are not suitable to handle a
large number of irrelevant descriptors. Thus, the selection of
relevant/informative descriptors plays a crucial rule in the
construction of QSAR/QSPR models. The objectives of selecting descriptors based on importance are manifold: (I) to
alleviate overfitting and enhance QSAR/QSPR performance;
(II) to provide faster and more cost-effective models; and
(III) to gain deeper insight into the underlying chemical
structures of natural products [143]. Currently, there are
three major methods of feature selection: filter, wrapper, and
embedded approaches [143, 144]. Filter approaches assess
the relevance of descriptors by ranking a feature relevance
score and filtering a feature relevance score, such as the ttest. Subsequently, the top-ranked informative features are
used to construct a predictive model. This approach considers the intrinsic properties of the data and ignores the interaction with the model. Filter techniques are simple and fast,
with little computational complexity, and they are also easy
to manipulate for very high dimensional data sets. However,
these techniques are independent of the prediction model.
Instead of focusing on gaining an informative feature independently of the model selection step, wrapper approaches
Computer-Aided Drug Design of Bioactive Natural Products
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
1787
were proposed to mitigate this issue by embedding the model
within the candidate feature subset. Some examples of wrapper approaches include sequential forward selection (SFS)
[145], sequential backward elimination (SBE) [145] and genetic algorithm [146]. Advantages of the wrapper approaches
include their interaction between the candidate feature subset
and the model selection, whereas a common drawback of
these techniques is that they have a higher risk of overfitting
than filter techniques and are computationally intensive. In
the last category of feature selection techniques, termed embedded approaches, the selection of the informative subset
features is built into the model. Similar to wrapper approaches, embedded approaches have the advantage that they can
include the interaction with the classification model; however, they are far less computationally intensive than wrapper
methods. Some examples of embedded approaches included
decision tree [147], logistic model tree [148] and random
forest approaches [149]. (Table 1) provides a summary of the
feature selection methods, showing the most prominent advantages, disadvantages, and some examples for each method.
concepts of popular machine learning techniques that are
used for construction of QSAR/QSPR models.
Dividing the Data into Training and Testing Sets
scriptor.A schematic representation the k-NN method is illustrated in (Fig. 3A).
A simple method involving a classification task is the kNN algorithm. This algorithm is conceptually based on a
distance function, such as the Euclidean distance, to measure
the similarity between a pair of data. Given a data set
D = x1 ,..., x N , where x j ∈ ℜ N and N is the number of molecular descriptors, a positive integer k, and a new datum x
to be classified, the k-NN algorithm finds the k nearest
neighbors of x in D, denoted as k-NN( x ), and returns the
dominating class label in k-NN( x ) as the label of x . Given
descriptors of two compounds (e.g.,
xi and x j ), the Euclide-
an distance Dist ( xi , x j ) is
Dist ( xi , x j ) =
∑ ( xin − x jn )2
n =1
where
(1)
N
i
th
xin is the i compound with the nth molecular de-
To alleviate the overfitting problem, a QSAR/QSPR
model must perform well on both training and testing sets to
be an effective and efficient model. Currently, there are a
number of splitting algorithms, such as Kennard and Stone,
Dublex and k-means sampling. These three algorithms were
implemented with the R program within the prospectr software package. An introduction to the prospectr software
package can be downloaded at no cost from http://cran.rproject.org/web/packages/prospectr/index.html.
The MLR method attempts to model the relationship and
behavior between a set of molecular descriptors X and a
quantitative value Y by fitting a linear equation to observed
data. In MLR analysis, stepwise regression is used to select
the most informative descriptor and improve the performance of the QSAR/QSPR model. Formally, the
QSAR/QSPR model constructed from the MLR method is
Establishing the QSAR/QSPR Model
y i = β 0 + β 1 x i1 + β 2 x i 2 + ... + β N x iN = ∑ β n x in + β 0 (2)
N
The construction of a QSAR/QSPR model is based on the
principal idea of machine learning. Currently, a few wellknown QSAR/QSPR models based on machine learning
techniques include multiple linear regressions (MLR), partial
least square (PLS), k-nearest neighbor (k-NN), artificial neural network (ANN), support vector machine (SVM), decision
tree (DT), and random forests (RF). All of these methods
have been reported in many applications of QSAR/QSPR
modeling. Machine learning tasks are typically classified
into two broad categories consisting of classification and
regression tasks. Classification tasks aim to discriminating a
variable Y into its class or property, where the Y variable
could be classified into two and more than two classes,
which are called binary and multi-class classification, respectively. In contrast, the regression task primarily focuses
on predicting the value of the variable Y with a numerical
output. The MLR, PLS, ANN, SVM, and RF methods can be
utilized in both classification and regression tasks, whereas
k-NN and DT are used only in the classification task. Additionally, the tasks of machine learning could be further divided according to their inclusion (supervised learning) or
omission (unsupervised learning) of the variable Y. All examples of QSAR/QSPR models are commonly used in supervised learning tasks, whereas a well-known unsupervised
learning method is principal component analysis (PCA).
(Fig. 3) displays the schematic representation of the major
n =1
where yi is the output value. To obtain the MLR parameter
β i , the ordinary least squares (OLS) approach is used by
minimizing the sum of the actual and predicted values to
give a loss function (actual value – predicted value).
In practice, it is laborious to directly manipulate and visualize high-dimensional data. Rather than analyzing the original dimension of data X, the importance of the extracted
variable is more useful. In this regard, PCA is likely the most
popular unsupervised learning technique based on a statistical approach that reduces the dimensionality of the data set
to a smaller subset known as principal components (PCs)
while preserving its dominant characteristics (variance)
[150]. The major goals of PCA are as follows: 1) to extract
the most information from X variable; 2) to analyze the pattern of X and Y variables; and 3) to remove an outlier(s).
(Fig. 3B) shows the scores and loading plots derived from
PCA approach.
Practically, if we have more variables (i.e., molecular descriptors) than compounds, the MLR method will not be a
suitable option. Further, the OLS approach might provide an
unstable parameter β i , which is difficult to interpret. The
PLS method was proposed to solve a large number of variables. This approach is the most commonly utilized approach,
1788
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
Table 1.
a
Prachayasittikul et al.
Summary of feature selection approaches.
Model Search
Advantage
Disadvantage
Examples
Filter
-Independent of the classifier
-Better computational complexity than wrapper
methods
-Ignores interaction with the classifier
-t-test
Wrapper
-Interacts with the classifier
-Model feature dependencies
-Risk of overfitting
-Classifier dependent selection
-Genetic algorithma
-Sequential forward selectionb
-Sequential backward selection b
Embedded
-Better computational complexity than wrapper
methods
-Interacts with the classifier
-Model feature dependencies
-Classifier dependent selection
-Decision tree c
-Logistic model tree d
-Random forests e
Reference [146], b Reference [145], c Reference [147], d Reference [148], e Reference [149].
Fig. (3). Schematic overview of commonly used machine learning techniques comprising of k-nearest neighbor (A), Principal component
analysis (B), artificial neural network (C), support vector machine (D), decision tree (E), and random forests (F).
rather than MLR or PCA. Practically, PLS (projection to
latent structures), is used to establish the correlation of a
matrix of X variables that have high variance and good correlation with a matrix of Y variables. The correlation approximation is achieved by simultaneously projecting the X and Y
matrices on lower dimensional spaces that are represented by
PLS components. The idea behind the PLS model is to cal-
culate the PLS component T by decomposing the block of
X = TP + residuals and predict the response variable
Y = TC + residuals, where P and C are the inverse of loading
scores of X and Y, respectively. Additional details of the PLS
model can be found in references [151-153].
The k-NN, MLR, and PLS methods are suitable for modeling linear relationships between the variables X and Y;
Computer-Aided Drug Design of Bioactive Natural Products
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
thus, when data sets possess a nonlinear relationship, these
three methods might not perform well. ANN was proposed
for use with nonlinearly separable data sets. Computational
models of this method were inspired by the human central
nervous system. The details of ANN evolved from the perceptron concept, which is one of algorithms used for supervised classification [154]. Mathematically, ANN is represented by a nonlinear weighted sum:
N
y i = θ (∑ β n xin + β 0 )
(3)
n =1
where θ (•) is the activation function. The sigmoid function θ (•) is a commonly used activation function in ANN
and refers to the special case of the logistic function defined
by
1
θ(X ) =
(4)
N
∑ β n xin + β 0
1 + e n =1
The prediction result possesses a value of 1 if Eq. 4 is
greater than the threshold value; otherwise, the prediction result is 0. Because the goal of any supervised learning algorithm is to construct a model that performs well on
both internal and external sets, backpropagation, also called
backward propagation, is a commonly used method for training ANN by using an optimization method such as gradient
descent. This method calculates the gradient of a loss function
with respect to all the weights or parameters β i in the network. The gradient is fed to the optimization procedure to
provide more accurate weights and to minimize the loss function. This method has been applied in both classification and
regression tasks. (Fig. 3C) shows the most common structure
of ANN composing of three layers, i.e. input, hidden, and output layers, with full inter-connection (Table 2). Additional
details of the ANN method can be found in references [155]
and [156].
Table 2.
1789
SVM was originally developed for classification by Cortes and Vapnik [157]. This method attempts to construct a
separating hyperplane that maximizes the margin between
the two classes of data sets. Intuitively, a good separation or
classification occurs when the hyperplane has the greatest
distance to neighboring data points of both classes because a
larger margin leads to lower values of the loss function of
the classifier and also accurately predicts each data point. To
easily understand SVM, a linear model (i.e., Eq. 2) can be
used for a binary classification problem given a data set D.
To achieve the maximizing margin, the optimization approach is defined as
1 2
w
2
s.t. yi (β i xi + β 0 ) > 1
min w, β
(5)
This method has w, β as its parameters. Previously,
SVM has been successfully applied in QSAR modeling by
utilizing the -insensitive loss function [157, 158] as follows:
Lε (y, f (x,β ))={
|y−f (x,β )|−ε ,y−f (x,β )|≥ε
0
,y−f (x,β )|<ε
(6)
where y is the actual value, f ( x, β ) is the predicted value
(in which the simple form is
f (x) , where
x = ( x1 , x2 ,..., xn )) and ε
is the insensitivity parameter. In
SVM regression, the basic application of nonlinearly separable data is to map the original dimension of the input data
(input space) into higher dimensional space (feature space)
by using mapping functions. The mapping function
φ ( x) : x ⊂ ℜ N → ℜ M , where N<<M, is performed by de-
Summary of the QSAR/QSPR models.
Method
Linear/
Non-linear
Classification/
Regression task
Advantage
Disadvantage
k-NN
Linear
Classification
Simple
Unstable and unreliable
MLR
Linear
Regression
Simple
Limitation for data with huge numbers of
features
PLS
Linear
Both
Performs well on data with huge numbers of features
Linear model
ANN
Non-linear
Both
Performs well on nonlinear data
Black-box method
SVM
Non-linear
Both
Most powerful method for both classification and regression
Black-box method
DT
Non-linear
Classification
Highly interpretable
Requires a large number of training instances
RF
Non-linear
Both
More confident estimate
Computationally intensive
Explanation of abbreviations: multiple linear regressions (MLR), partial least square (PLS), k-nearest neighbor (k-NN), artificial neural network (ANN), support vector machine
(SVM), decision tree (DT), and random forests (RF).
1790
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
fining the inner product between each pair of data points,
i.e., two compounds, in the data set of the feature space
through the kernel function K ( x i , x j ) . The overview of
SVM and its kernel function is shown in (Fig. 3D). The kernel function K ( x i , x j ) can be expressed as a similarity
measurement between the training data set, which is defined
as:
K ( xi , x j ) = φ ( xi ) T φ ( x j ) =
N
∑φ ( x )
i
T
φ(x j )
(7)
The most popular used kernels include the linear kernel
Φ(xi )T Φ(x j ) the polynomial kernel (1+ Φ(xi )T Φ(x j ))d , where d
= 2, 3, and 4 (it should be noted that d = 1 for a linear kernel); and the radial basis function (RBF) kernel
exp(−γ ( x i − x j )) , where γ is greater than 0.
Although the ANN and SVM methods have achieved
outstanding performance, these approaches make interpretation of the contained system difficult, which is why they are
called black-box methods. DT, also called tree induction,
was proposed to mitigate this problem by using a set of estimated rules. The decision tree has an efficient built-in feature importance estimator. The C4.5 algorithm is the generally used approach for several classification tasks [147, 159].
The construction of the DT model requires the following: 1)
all samples in the internal set belong to a single class; 2) the
tree depth is close to maximum; and 3) the number of classes
in the terminal node is less than the minimum number of
classes of the parent nodes. In general, the root node is selected from a variable with the highest information gain,
whereas the other node or internal node provides the second
highest information gain, etc. The information gain of variable v (Gainv ) is calculated as follows:
| Dv |
I ( Dv ) (8)
v∈V | D |
Gainv = ∑ − p(C j ) log 2 p(C j ) − ∑
j =1
internal set. In particular, there are two measurements to
select an informative variable: the mean decrease of the Gini
index (MDG) and the prediction accuracy (MDA). The
MDG has been commonly utilized to estimate feature importance because the MDG is suggested to be more robust
than the mean decrease of accuracy [163]. The MDG can be
defined as follows:
MDGI (v) = 1 − ∑ p 2 (C j | v)
(9)
j
i , j =1
N
Prachayasittikul et al.
where Gainv is the information gain of feature v on the remaining data Dv ⊂ D , and p(C j ) is the probability of the
relative frequency of class j ( C j )[147, 160]. The decision
tree can perform well if enough internal sets are available.
The structure of DT with three nodes used to classify a compound into either active or inactive class is shown in (Fig.
3E). The logistic model tree was proposed to alleviate this
problem and can be applied to classification and regression
problems [148, 161].
The RF method is an effective and efficient prediction
method based on an ensemble model for solving the classification and regression problems. Breiman first proposed this
ensemble method as belonging to a machine learning technique [149]. This method improved the predictive performance of classification and regression trees [162] (CART)
by growing many weak CART trees. Every tree is constructed by using a fixed number of randomly selected features for
tree splitting and is based on a bootstrap sample of the whole
where p (C j | v ) denotes the estimated class probabilities for
feature v in the current decision tree. The feature with the
largest value of MDG is the most important feature because
it contributes most to prediction performance. This ensemble
approach improved predictive performances of CART by
selecting from the prediction results of many decision trees
[164]. (Fig. 3F) shows the top 13 molecular descriptors
ranked by MDG and MDA.
Validating the QSAR/QSPR model: After constructing
the QSAR/QSPR model, the internal validation of the proposed model is crucial for assessing the reliability of the
models and their ability to accurately predict biological activities or chemical properties. In the classification task, four
measurements were generally used to evaluate the prediction
performance of the proposed QSAR/QSPR model using
cross-validation (CV), namely accuracy (ACC), sensitivity
(SEN), specificity (SPEC), and Matthews correlation coefficient (MCC), which are defined in the following equation:
TP + TN
× 100
(TP + TN + FP + FN )
TP
Sensitivity =
× 100
(TP + FN )
TN
Specificity =
× 100
(TN + FP)
Accuracy =
MCC =
TP × TN − FP × FN
(TP + FP )(TP + FN )(TN + FP )(TN + FN )
(10)
(11)
(12)
(13)
where TP, TN, FP and FN represents the numbers of true
positive, true negative, false positive and false negative, respectively [165]. As for the regression task, various statistical parameters are used for evaluating the robustness of
QSAR/QSPR models. Often, the criteria of goodness-of-fit
(R2 assessed on the whole internal set) and goodness-ofprediction ( R 2pred assessed by various validation procedures)
are characterized by the coefficient of determination R2 or
R 2pred and root mean square error RMSE [125]. The R2 and
RMSE are defined as:
⎡
R2 = ⎢
⎢
⎣
∑ ( y − y )∑ ( ~y − ~y )
∑ ( y − y ) ∑ ( ~y − ~y )
i
i
2
i
i
2
⎤
)⎥
⎥
⎦
2
(14)
n
RMSE =
∑ ( y − ~y )
i =1
n
2
(15)
Computer-Aided Drug Design of Bioactive Natural Products
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
where y , ~
y , y and ~
y are the values of actual, predicted,
average value of actual and average value of predicted activities, respectively, while n is the number of compounds. Although, high R 2pred values are frequently used as one of the
criterion for selecting robust QSAR/QSPR model, it may not
afford the most reliable model. Thus, the whole dataset must
be divided as internal and external sets, as mentioned above.
Tropsha suggested that acceptable QSAR/QSPR models
should satisfy the following conditions [166-168]:
2
R pred
> 0.6
(16)
2
Rext
> 0.5
(17)
0.85 ≤ k , k ' ≤ 1.15
(18)
2
2
( R pred
− R02 ) / R pred
< 0.1
(19)
2
2
2
( R pred
− R ' 0 ) / R pred
< 0.11
2
where R 2pred and Rext
are the correlation coefficient between
the predicted and actual activities of compounds as assessed
by several validation and external sets, respectively, k and
k ′ are the regression coefficients obtained from y r 0 = k~
y
2
2
and ~
y r 0 = k ′y , respectively, and R0 and R ' 0 are calculated
as follows:
∑ ( ~y − y )
=1−
∑ ( ~y − ~y )
∑ ( y − ~y )
=1−
∑ ( y − y)
r0
R
2
0
i
2
(20)
i
r0 2
R
'2
0
i
i
2
goal of this validation is to provide a reliable performance
evaluation and simulating the general performance of the
model on compounds with unknown activity/property. Practically, in order to perform a QSAR/QSPR model on new
screening compounds, its domain of applicability should be
calculated [170]. The domain of applicability can be characterized using the Euclidean distance (Eq. 1) amongst all possible pairs between internal and external sets [167, 171].
Prediction of the new screening compound is considered
acceptable when the distance of the new compound to its
nearest neighbor in the internal set was lower than that of the
predefined applicability domain. The second approach in
defining the domain of applicability of a QSAR/QSPR model can be evaluated from the leverage value of each compound [172]. In cases when compounds have larger leverage,
it means that the prediction may be a substantial extrapolation of the QSAR/QSPR model and may not be reliable. In
summary, the new screening compound falling into its domain of applicability may be considered reliable. Finally, the
Y-randomization test or Y-scrambling is the most commonly
used parameter for regression task in assessing models for
chance correlation [167]. This test is performed by constructing a model in which the Y variable (i.e. activity of compound) is randomly shuffled but the X variable (i.e. molecular descriptor) is kept constant. The model is then retrained
to generate a new scrambled model. If the original model has
no chance correlation, the result of scrambled models could
be expected to provide low R 2 and R 2pred values.
Pharmacophore and CoMFA
2
i
1791
(21)
i
where ~
yi is the ith compound from the external set. The
basic form of cross-validation is known as the k-fold crossvalidation (k-fold CV). For example, for a 10-fold CV experiment, the data are first partitioned into 10 equally (or nearly
equally) sized segments or folds, then 9 segments are used
for training and the remaining segment is used for validation.
Finally, the results are then averaged across the 10 experiments. Leave-one-out cross-validation (LOO-CV) is a special case of k-fold CV where k equals the number of instances in the data. The LOO-CV method is a widely used approach when the available data are rare, especially in bioinformatics where only a few data points are available [169].
There are several statistical techniques to evaluate the
predictive ability of a QSAR/QSPR model including external
validation, Y-randomization test, domain of applicability and
the William plot [124]. External validation is commonly
used to evaluate the external predictivity of a QSAR/QSPR
model by leaving out a subset of data at the onset of the experiment while the remaining internal set is used for evaluating optimal parameters of learning algorithms. The main
A pharmacophore is defined as a two- or threedimensional arrangement of the chemical features of compounds that are required for optimal interaction with the protein target [173] and contribute to biological responses [174].
The pharmacophore concept was first introduced in the
1900s by Ehrlich. The concept proposed by Ehrlich stated
that a pharmacophore is not the same as a functional groups
of molecules; rather, it is a molecular scaffold that carries
essential features responsible for the compounds’ bioactivity
[175]. Pharmacophores can be grouped into two classes on
the basis of the method that is used to obtain them [173]. The
first class is structure-based pharmacophores, which is based
on probing the possible interaction points between the ligand
and the target [173]. The second class is ligand-based pharmacophores, based purely on the structure and binding data
of the ligand to the target without consideration of the threedimensional structure of the target proteins for which many
active molecules are superimposed to extract the common
features that are crucial for bioactivity [176, 177]. Thus,
pharmacophore modeling intuitively produces results pertaining to the interactions between chemical ligands and target proteins in three dimensions, and the resulting features
can be derived by computational algorithms that extract information from large quantities of data [178]. Conceptually,
common structural features of bioactive and bioinactive
chemical compounds of protein targets are identified by considering many features of the compounds, e.g., the spatial
arrangement of features, physicochemical properties, steric
characteristics, bonding capabilities and quantum chemical
properties of chemical compounds, including hydrogen-bond
1792
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
Table 3.
Prachayasittikul et al.
List of softwares related to pharmacophore modeling.
Name
Description
Software
Format
Availability
URL
Align-it
(Pharao)
Pharmacophore alignment
Standalone
Free
http://silicos-it.be
Catalyst
Pharmacophore modeling
Standalone
Commercial
http://accelrys.com/products/discovery-studio/pharmacophore.html
CATSlight2
Topological pharmacophore
descriptor for scaffold hopping and target identification
Web service
Free
http://modlab-cadd.ethz.ch/software/catslight2/
FLAP
Fingerprints using pharmacophoric features
Standalone
Commercial
http://www.moldiscovery.com/software/flap/
LigandScout
Pharmacophore modeling
Standalone
Commercial
http://www.inteligand.com/ligandscout/
MOE
Pharmacophore modeling
Standalone
Commercial
http://www.chemcomp.com/MOE-Pharmacophore_Discovery.htm
MolSign
Pharmacophore modeling
Standalone
Commercial
http://www.vlifesciences.com/products/Functional_products/Molsign.php
PharmaGist
Pharmacophore detection
Standalone/
Web service
Free
http://bioinfo3d.cs.tau.ac.il/pharma/
Pharmer
Pharmacophore search
Standalone
Free
http://smoothdock.ccbb.pitt.edu/pharmer/
PharmMapper
Drug target identification
Web service
Free
http://59.78.96.61/pharmmapper
Phase
Pharmacophore modeling
Standalone
Commercial
http://www.schrodinger.com/Phase/
Quasi
Pharmacophore modeling
Standalone
Commercial
http://www.denovopharma.com/page2.asp?PageID=485
UNITY
Pharmacophore modeling
Standalone
Commercial
http://www.certara.com/products/molmod/sybyl-x/simpharm/
ZincPharmer
Pharmacophore search for
purchasable compounds
from ZINC database
Web service
Free
http://zincpharmer.csb.pitt.edu
donors, hydrogen-bond acceptors, charged groups, hydrophobic interactions [179] and the three dimensional arrangement of the target protein. The pharmacophore models
were built and validated with different compound series to
determine whether the active compounds fit the pharmacophore. Finally, a reliable and robust pharmacophore model
was obtained by returning a large series of compounds with
preferable binding modes to the target [180]. Thus, the
pharmacophore modeling merges information from structure-activity with the active sites of protein targets [181].
The capability of binding to the target protein of such chemical compounds indicates that some portions of the compounds are important and are responsible for favorable interactions with the target [182]. By using the insights derived
from the pharmacophore model, the important functional
groups that are essential for interacting favorably with the
target and that contribute to bioactivity can be identified.
Pharmacophore features have been widely used for virtual
screening, de novo design and lead optimization [183].
CoMFA (Comparative Molecular Field Analysis), also
known as the 3D-QSAR method, can be used to identify
pharmacophores by correlating ligand 3D structures with
their binding activities. The structures of ligands are superimposed to identify common features that are responsible for
their biological activities without requiring 3D structures of
target proteins. Typically, parameters such as steric energies,
electrostatic interactions and the location of an atom at lattice
intersections, together with bioactivities, are used to build
these predictive models [184]. Because CoMFA will usually
extract a large number of parameters, the partial least square,
a commonly used liner modeling method, simultaneously
projects the extracted parameters from CoMFA with bioactivities into latent variables to correlate multiple parameters
with bioactivities. The extent of the parameters’ influence on
bioactivities is indicated by regression coefficients [185].
DISCO (DIStanceCOmparisons) is the first automated
pharmacophore modeling method that can systemically analyze and match the conformation of diverse molecules [186]
by using the Bron-Kerbosh clique-detection algorithm [187].
The superposition rule of bioactive conformation is used to
identify common pharmacophoric features. However, the
identification is based on the distance points of intramolecular interactions of conformations within chemical compounds without consideration of the three dimensional spatial arrangement of the target [187]. DISCO has been successfully utilized to identify pharmacophores of dopaminergic agonists [187], ligands of nucleoside transporters hCNT1
[188], antihypertensive drugs [189], cAMP PDE III inhibitors [190], neuronal nicotinic receptor agonists [191], inhibitors of vitamin D hydroxylases [192] and cGMP phosphodiesterase inhibitors [193].
Computer-Aided Drug Design of Bioactive Natural Products
Currently, many automated pharmacophore generator
software programs have been developed for application in
drug discovery and development [183], such as GASP [194],
HipHop [195], HypoGen [196], MOE [197], PHASE [198]
and GALAHAD [199]. LigandScout is an integrated platform for 3D virtual screening and pharmacophore modeling.
It considers ligand-protein interactions, including hydrogen
bonding, π- π stacking, Van der Waals, charge transfer, electrostatic and hydrophobic interactions [200].
Molecular Docking and Molecular Dynamics Simulations
There has been an explosive growth in the available
structural data for proteins by X-ray crystallographic and
NMR spectroscopic studies and derived from large amounts
of genomic and proteomic data by theoretical modeling. For
this reason, discovering new drug targets relies on accurate
modeling of these data in rational drug design because information from both protein structures and their ligandbinding sites can be exploited. In this case, two widely used
methods, molecular docking and molecular dynamics simulation, play a major role in these approaches and are usually
combined to investigate interactions of small molecules with
the protein target at the atomic level [201].
Molecular docking is a term used for the computational
scheme that attempts to search for the possible binding
modes of a ligand with its receptor [202]. Docking algorithms have been developed to generate a comprehensive
conformational set of protein-ligand complexes, which subsequently scores them according to their stability. Several
factors influence the process of a ligand binding to its protein, including thermodynamic and solvation contributions
and the charge distributions of the protein and ligand mole-
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
1793
cules [203]. In any docking scheme, two conflicting requirements should be balanced: the desire for an accurate
procedure and the desire to keep the computational demands
at a reasonable level. Thus, the ideal docking protocol should
explore all available degrees of freedom for a particular system to reach the global minimum in the free energy of binding within an amount of time comparable to other laboratory
working computations [204]. In contrast to molecular docking, molecular dynamics (MD) simulations, which represent
one of the most versatile computational techniques for studying the dynamics of biomolecules, are more computationally
expensive and sophisticated. These simulations generate a
set of conformations of a biomolecule by iteratively integrating (numerically) the equations of motion for a specific potential function with certain initial and boundary conditions
[205]. A structural ensemble generated from an MD simulation can be used to explore the conformational space of biomolecule, to calculate thermodynamic quantities and to estimate the free energy of biological processes [206]. In the
prediction of the strength of non-bonded interactions, the
MD technique has been widely used in free energy binding
calculations, which cover a broad range of accuracies and
computational requirements. Other computationally expensive but highly accurate methods include the free energy
perturbation (FEP) and thermodynamic integration (TI)
methods, whereas the linear interaction energy (LIE) and
molecular mechanics/Poisson-Boltzmann surface area
(MM/PBSA) methods, which increase computational speed
at the expense of accuracy, have been developed more recently [207].
Once the structure of a protein target is known, the process of rational drug design follows a well-established protocol, as shown in (Fig. 4). As mentioned previously, molecu-
Fig. (4). Schematic representation of the protocol combining molecular docking and molecular dynamics simulations that is applied during
rational drug design such that the structure of the protein target can be experimentally or theoretically obtained. (Hits = compounds that can
bind to a target, Leads = hit compounds with more preferable potency, MD = molecular dynamics).
1794
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
lar docking techniques can be applied during the high
throughput virtual screening stage to scan a large compound
library and identify small molecules that are more likely to
bind to the protein target. This initial screening employs of
inexpensive and fast docking algorithms to evaluate the
binding affinities. Subsequently, the selected compounds
will be subjected to additional docking experiments using
more accurate scoring functions. Once a few hit compounds
are identified, MD can then be used to refine such docked
complexes, which can account for effects of induced fit and
explicit solvation and can test the stability of the complex
over (simulated) time. When the well-equilibrated MD ensemble has been generated, it can be used for the calculation
of more accurate binding free energies, e.g., LIE and
MM/PBSA, which are expected to provide much better scoring than the simple algorithms used during the initial docking step. Therefore, the two techniques in the protocol, in
which molecular docking is used for the fast screening of a
large library of compounds and MD simulations, are sequentially applied to optimize the structure of the final complexes. Finally, accurate binding free energies are then calculated, which makes for a rational approach that helps to improve the drug discovery process [208, 209].
The use of combined docking and MD methods has been
broadly applied for the identification of new therapeutic
agents from compounds of natural origin and the optimization of new lead candidates derived from natural compounds.
Recent examples using molecular docking-based virtual
screening for the discovery of potent inhibitors from natural
product databases have been extensively reviewed [210].
The identified bioactive compounds target many biological
processes, including enzyme-substrate interactions, receptorligand interactions and DNA interactions [210]. The most
promising targets for natural compounds are protein kinases,
and others include DNA methyltransferases [211], aldose
reductase [212], viral enzymes [213], and beta amyloid (Aβ)
peptides [214]. Several studies based on biophysical and
docking experiments clearly demonstrate that various flavonoids including myricetin, quercetin, caffeic acid, daidzein,
delphinidin, and procyanidin can bind directly to several
protein kinases such as Akt/protein kinase B (Akt/PKB),
Fyn, Janus kinase (JAK) 1, mitogen-activated protein kinase
Prachayasittikul et al.
kinase (MEK) 1, phosphoinositide 3-kinase (PI3K), mitogenactivated protein kinase kinase (MKK) 4, and Raf1. Notably,
all of these kinases control multiple cell signaling pathways
in oncogenesis [215]. Recently, several compounds from the
traditional Chinese medicine (TCM) database [216] were
successfully identified as potent inhibitors of human epidermal growth factor receptor (HER) 1, and 2 tyrosine kinases
that have been known to be associated with several types of
cancer have been identified by combining docking, 3DQSAR and MD simulations [217-219].
As mentioned, MD simulations combined with the
MM/PBSA method can be widely used to obtain detailed information on the binding efficacy of drug-target interactions.
This approach has also been applied to investigate the inhibitory efficiency of natural compounds to several protein targets,
including cyclin dependent kinase (CDK) 6 [220], HER2 kinase [221], and Aβ peptides [222, 223]. The theoretical results
are in good agreement with experimental results, suggesting
the efficiency of the method to predict accurate binding affinities that are beneficial for the validation of drug-target complexes. In addition to the free energy calculations, MD can
provide valuable information by giving dynamical information
of protein structures. MD has been applied to study the dynamic events of targeting Aβ peptide aggregation by morin,
one of the most effective anti-aggregation flavonoids [214].
Lemkul et al. [224] conducted long MD simulations to
demonstrate that morin can destabilize the Aβ42 protofibrils by
blocking the attachment of an incoming peptide onto the
growing end of an Aβ42 fibril and can disrupt the crucial interpeptide salt bridges, which are an important contribution in the
stability of the Aβ42 protofibrils. Extended work from the same
group also showed that morin can inhibit the early stages of
Aβ peptide aggregation by affecting the tertiary and quaternary structure of premature Aβ40 and Aβ42 monomeric and
dimeric states that give rise to different structures, which presumably result in o-pathway aggregates that may have reduced toxicity compared to untreated peptides [225]. Consequently, the examples given here demonstrate how one can
utilize MD as a tool to understand the mechanism by which
potent natural compounds act on the structure of protein targets. For example, (Fig. 5). shows the binding pose of natural
product curcumin I at the active site of HER2 kinase.
Fig. (5). Binding pose of natural compound (curcumin I, Diferuloylmethane) at the active site of HER2 kinase previously investigated by
Yim-Im et al. [221]. The compound and amino acid residues are represented in sticks with larger and smaller sizes, respectively (carbon,
grey; nitrogen, blue; oxygen, red). Hydrogen bond and hydrophobic interactions are indicated in green and pink dashed lines, respectively.
Computer-Aided Drug Design of Bioactive Natural Products
CONCLUSION
The prestige of traditional medicine has been recognized
by its effectiveness in curing diseases and its ability to improve the quality of life from antiquity [226, 227]. In the
past, the use of natural products as therapeutic agents was
restricted in developing countries and rural regions where
medical facilities were inaccessible and unaffordable [15].
Recently, the use of natural products has become more popular and acceptable worldwide, especially in developed countries where advanced modern medicine has been developed
[228]. People pay more attention to traditional medicine and
natural products because of their concern about the adverse
side effects of synthetic drugs [229-231]. Furthermore, most
of the population in the globalization era suffers from lifestyle-related, stress-related and aging diseases. These chronic
diseases are related to the changing lifestyle in which society
is more concerned about the way individuals eat and live
[230]. The return of herbal medicines can be observed from
the parallel use of complementary and alternative medicines
with modern medicine to improve treatment outcomes [232],
as well as via the trends of using natural products for the
prevention and promotion of good health, i.e., dietary supplements [233, 234].
Many countries have established unique herbal medicine
systems with regards to their cultural history, ecology and
medical anthropology [15]. Most of the traditional medicine
systems often prescribe combinations of herbal mixtures, and
their therapeutic effects are based on synergistic or antagonistic effects among each other [15]. Although it is widely
believed that herbal medicines are safe, serious undesired
side effects have been reported [235]. Hence, in an attempt
to contribute to the health of the world’s population, the
World Health Organization (WHO) has encouraged a prime
focus on herbal medicines to standardize regulations across
countries and promote their safety and efficacy [228, 235].
Natural products are major sources of inspiration for the
discovery of new drugs and are of great value to the field of
drug development [109]. With respect to the power of Mother
Nature, all organisms select chemicals for synthesis, consumption and utilization based on evolutionary advantage [95]. Diverse types of biologically relevant privileged structures are
provided by natural products, especially plants [104]. It should
be noted that some of these scaffolds share common core
structures with different substituent patterns, which give rise
to their different bioactivities within the same organism or
across species [84]. In other words, it can be stated that scaffolds of natural products are evolutionary-chosen [84]. Hence,
scaffolds from natural products serve as structural starting
points to explore the biologically relevant chemical space [86],
and the modification to these privileged structures is required
for preferable therapeutic properties [97].
Computational approaches are fundamental for drug discovery and development, with no exception for naturally
derived drugs. Databases have been developed to aid drug
design and discovery and participate in the identification of
hits and druggable targets. Computational approaches (i.e.,
molecular docking, QSAR, QSPR, chemoinformatics and
data mining) can provide insights into many aspects of target
proteins, naturally occurring privileged structures, and protein-ligand interactions, which are essential for the discovery
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
1795
and development of novel lead compounds enriched with
bioactivities. Because most natural products are not designed
for human use, the transformation of naturally occurring
bioactive compounds into human drugs requires modification and evaluation by multidisciplinary teams of experts.
Computational tools provide understanding on structureactivity or structure-property relationships, which are useful
guidelines for the design and synthesis by organic/medicinal
chemists to obtain compounds with improved potency, selectivity, and drug-like properties and reduced undesirable side
effects. Furthermore, computational chemogenomics (related
concepts include proteochemometric modeling, polypharmacology, systems pharmacology) facilitates the seamless integration of bioinformatics and cheminformatics by allowing
the interaction of several proteins and several ligands to be
investigated. Such approach has great potential for drug repositioning, target identification, ligand profiling and receptor deorphanization.
To place this field of research into greater perspective,
populations worldwide face chronic and multifactorial diseases relating to changing lifestyles and aging conditions.
Behavior-related diseases are becoming the foremost health
issue that must be addressed. Most chronic diseases arise
from unhealthy lifestyles and continual exposure to harmful
chemicals. This situation ought to stimulate people to have
greater concern about how they spend their life. Healthy lifestyles and eco-friendly products are becoming fashionable
for new generations. In addition, the polypharmacologybased principle of traditional medicine is expected to provide
favorable treatment outcomes against multifactorial diseases.
Therefore, traditional medicine systems and natural products
are returning as an alternative method of treatment, with emphasis on their safety because they are naturally derived.
Furthermore, significant attention has been given to natural
products because of their influence on human well-being, as
they yield beneficial sources of bioactive ingredients for
cosmeceuticals and nutraceuticals. In summary, natural
products are of great benefit to mankind, and extensive research on these natural treasures would provide substantial
impact for the betterment of society.
CONFLICT OF INTEREST
The author(s) confirm that this article content has no conflict of interest.
ACKNOWLEDGEMENTS
This research project is supported by the annual budget
grant of Mahidol University (B.E. 2556-2558), Mahidol
University Talent Management Program to A.W. as well as
the Office of the Higher Education Commission and Mahidol
University under the National Research Universities Initiative.
REFERENCES
[1]
[2]
[3]
Solecki, R.S. Shanidar IV, a Neanderthal flower burial in northern
Iraq. Science, 1975, 190(4217), 880-881.
Cragg, G.M.; Newman, D.J. Biodiversity: A continuing source of
novel drug leads. Pure Appl. Chem., 2005, 77(1), 7-24.
Hartwell, J.L. Plants used against cancer. A Survey. Lloydia, 1967,
30, 379-436.
1796
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
Butler, M.S. The role of natural product chemistry in drug discovery. J. Nat. Prod., 2004, 67(12), 2141-2153.
Kinghorn, A.D.; Pan, L.; Fletcher, J.N.; Chai, H. The relevance of
higher plants in lead compound discovery programs. J. Nat. Prod.,
2011, 74(6), 1539-1555.
Hicks, S. Desert plants and people, 2nd ed.; The Naylor Company
Book Publisher of the Southwest: Texas, 1971.
Stocker-Worgotter, E. Metabolic diversity of lichen-forming ascomycetous fungi: culturing, polyketide and shikimatemetabolite
production, and PKS genes. Nat. Prod. Rep., 2008, 25(1), 188-200.
Dias, D.A.; Urban, S.; Roessner, U. A historical overview of natural products in drug discovery. Metabolites, 2012, 2(2), 303-336.
Jensen, A. Present and future needs for algae and algal products.
In: Fourteenth International Seaweed Symposium; Chapman,
A.R.O.; Brown, M.T.; Lahaye, M., Ed.; Springer: Netherlands,
1993, Vol. 85, pp.15-23.
Smith, E. Medicinal plants in folk tradition: An ethnobotany of
Britain and Ireland. HortScience, 2005, 40(3), 505-505.
Borlase, W.; Pool, P.;Turk, F. The natural history of Cornwall; E.
& W. Books, 1970.
Furuya, T.; Ikuta, A.;Syōno, K. Alkaloids from callus tissue of
Papaver somniferum. Phytochemistry, 1972, 11(10), 3041-3044.
Sneader, W.E., Drug Discovery (The History). In: Van Nostrand's
Scientific Encyclopedia; John Wiley & Sons, Inc., 2005.
Leonti, M. Traditional medicines and globalization: current and
future perspectives in ethnopharmacology. Front. Pharmacol.,
2013, 4(92), 1-13.
Touwaide, A.; Appetiti, E. Knowledge of Eastern materia medica
(Indian and Chinese) in pre-modern Mediterranean medical traditions: A study in comparative historical ethnopharmacology. J.
Ethnopharmacol., 2013, 148(2), 361-378.
Sharma, H.M. Contemporary Ayurveda. In: Fundamentals of Complementary and Alternative Medicine; Micozzi, M.S., Ed.; Saunders Elsevier: St. Louis, Missouri, 2011, pp. 495–508.
Samhita, S.; Sharma, P. Chaukhamba Orientalia; Varanasi: India,
1997.
Patwardhan, B.; Warude, D.; Pushpangadan, P.; Bhatt, N. Ayurveda and traditional Chinese medicine: A comparative overview.
Evid. Based Complement. Alternat. Med., 2005, 2(4).
Lu, A.; Jiang, M.; Zhang, C.; Chan, K. An integrative approach of
linking traditional Chinese medicine pattern classification and biomedicine diagnosis. J. Ethnopharmacol., 2012, 141(2), 549-556.
Park, J.; Park, H.J.; Lee, H.J.; Ernst, E. What's in a name? A systematic review of the nomenclature of Chinese medical formulae.
Am. J. Chin. Med., 2002, 30(02n03), 419-427.
Yin, C.S.; Ko, S.G. Introduction to the history and current status of
evidence-based Korean medicine: A unique integrated system of allopathic and holistic medicine. Evid. Based Complement. Alternat.
Med., 2014, 2014, 6.
Saralamp, P.; Chuakul, W.; Temsiririrkkul, R.; Clayton, T. Medicinal plants in Thailand. Vol. 1, 1996.
Yu, F.; Takahashi, T.; Moriya, J.; Kawaura, K.; Yamakawa, J.;
Kusaka, K.; Itoh, T.; Morimoto, S.; Yamaguchi, N.; Kanda, T. Traditional Chinese medicine and Kampo: A review from the distant
past for the future. J. Int. Med. Res., 2006, 34(3), 231-239.
Sumantran, V.N.; Tillu, G. Insights on personalized medicine from
Ayurveda. J. Altern. Complement. Med., 2012, 19(4), 370-375.
Shim, E.B.; Lee, S.; Kim, J.Y.; Earm, Y.E. Physiome and Sasang
constitutional medicine. J. Physiol. Sci., 2008, 58(7), 433-440.
Joshi, K.; Ghodke, Y.; Shintre, P. Traditional medicine and genomics. J. Ayurveda Integr. Med., 2010, 1(1), 26.
Prasher, B.; Negi, S.; Aggarwal, S.; Mandal, A.; Sethi, T.;
Deshmukh, S.; Purohit, S.; Sengupta, S.; Khanna, S.; Mohammad,
F.; Garg, G.; Brahmachari, S.; Consortium, I.G.V.; Mukerji, M.
Whole genome expression and biochemical correlates of extreme
constitutional types defined in Ayurveda. J. Transl. Med., 2008,
6(1), 48.
Ghodke, Y.; Joshi, K.; Patwardhan, B. Traditional medicine to
modern pharmacogenomics: Ayurveda Prakriti type and CYP2C19
gene polymorphism associated with the metabolic variability. Evid.
Based Complement. Alternat. Med., 2011, 2011.
Chen, S.; Lv, F.; Gao, J.; Lin, J.; Liu, Z.; Fu, Y.; Liu, Y.; Lin, B.;
Xie, Y.; Ren, X.; Xu, Y.; Fan, X.; Xu, A. HLA class II polymorphisms associated with the physiologic characteristics defined by
traditional Chinese medicine: linking modern genetics with an ancient medicine. J. Altern. Complement. Med., 2007, 13(2), 231-240.
Prachayasittikul et al.
[31]
[32]
[33]
[34]
[35]
[36]
[37]
[38]
[39]
[40]
[41]
[42]
[43]
[44]
[45]
[46]
[47]
[48]
[49]
[50]
[51]
[52]
Chao, M.; Zou, D.; Zhang, Y.; Chen, Y.; Wang, M.; Wu, H.; Ning,
G.; Wang, W. Improving insulin resistance with traditional Chinese
medicine in type 2 diabetic patients. Endocrine, 2009, 36(2), 268274.
Efferth, T.; Miyachi, H.; Bartsch, H.. Pharmacogenomics of a traditional Japanese herbal medicine (Kampo) for cancer therapy. Cancer Genomics Proteomics, 2007, 4(2), 81-91.
Matsumoto, C.; Kojima, T.; Ogawa, K.; Kamegai, S.; Oyama, T.;
Shibagaki, Y.; Kawasaki, T.; Fujinaga, H.; Takahashi, K.; Hikiami,
H.; Goto, H.; Kiga, C.; Koizumi, K.; Sakurai, H.; Muramoto, H.;
Shimada, Y.; Yamamoto, M.; Terasawa, K.; Takeda, S.; Saiki, I.. A
proteomic approach for the diagnosis of 'oketsu' (blood stasis), a
pathophysiologic concept of Japanese traditional (Kampo) medicine. Evid. Based Complement. Alternat. Med., 2008, 5(4), 463-74.
Won, H.H.; Lee, S.; Jang, E.; Kim, K.K.; Park, Y.K.; Kim, Y.J.;
Kim, Y.S.; Kim, B.Y.; Kim, J.Y.; Kim, J.W. A genome-wide scan
for the Sasang constitution in a Korean family suggests significant
linkage at chromosomes 8q11.22–23 and 11q22.1–3. J. Altern.
Complement. Med., 2009, 15(7), 765-769.
Kim, H.J.; Hwang, S.Y.; Kim, J.H.; Park, H.J.; Lee, S.G.; Lee,
S.W.; Joo, J.C.; Kim, Y.K. Association between genetic polymorphism of multidrug resistance 1 gene and Sasang constitutions.
Evid. Based Complement. Alternat. Med., 2009, 6 Suppl 1, 73-80.
Lee, T.G.; Koh, B.; Lee, S. Sasang constitution as a risk factor for
diabetes mellitus: a cross-sectional study. Evid. Based Complement.
Alternat. Med., 2009, 6 Suppl 1, 99-103.
Rates, S.M.K. Plants as source of drugs. Toxicon, 2001, 39(5), 603613.
Tilburt, J.C.; Kaptchuk, T.J. Herbal medicine research and global
health: an ethical analysis. Bull. World Health Organ., 2008, 86(8),
594-599.
Benzie, I.F.; Wachtel-Galor, S. Herbal medicine: biomolecular and
clinical aspects; CRC Press, 2011.
Taylor, J.L.S.; Rabe, T.; McGaw, L.J.; Jäger, A.K.; Staden, J.V.
Towards the scientific validation of traditional medicinal plants.
Plant Growth Regul., 2001, 34(1), 23-37.
DerMarderosian, A.; Beutler, J.A. The review of natural products:
the most complete source of natural product information.; Facts and
Comparisons, 2002.
Jiratchariyakul, W.; Mahady, G.B. Overview of botanical status in
EU, USA, and Thailand. Evid. Based Complement. Alternat. Med.,
2013, 2013, 13.
Hughes, J.P.; Rees, S.; Kalindjian, S.B.; Philpott, K.L. Principles of
early drug discovery. Br. J. Pharmacol., 2011, 162(6), 1239-49;
van de Waterbeemd, H.; Gifford, E. ADMET in silico modelling:
Towards prediction paradise? Nat. Rev. Drug Discov., 2003, 2(3),
192-204.
Mandal, S.; Moudgil, M.; Mandal, S.K. Rational drug design. Eur.
J. Pharmacol., 2009, 625(1-3), 90-100.
Fox, S.; Farr-Jones, S.; Sopchak, L.; Boggs, A.; Nicely, H.W.;
Khoury, R.; Biros, M. High-throughput screening: update on practices and success. J. Biomol. Screen, 2006, 11(7), 864-9.
Reymond, J.L.; Ruddigkeit, L.; Blum, L.; van Deursen, R. The
enumeration of chemical space. Wiley Interdiscip. Rev. Comput.
Mol. Sci., 2012, 2(5), 717-733.
Wikberg, J.E.S.; Eklund, M.; Willighagen, E.L.; Spijuth, O.; Lapins, M.; Engkvist, O.; Alvarsson, J. Drug discovery and development, In: Introduction of Pharmaceutical Bioinformatics; Oakleaf
Academic Publishing House: Stockholm, 2011, pp. 27-39.
Klopmand, G. Concepts and applications of molecular similarity,
by Mark A. Johnson and Gerald M. Maggiora, eds., John Wiley &
Sons, New York, 1990, 393 pp. Price: $65.00. J. Comput. Chem.,
1992, 13(4), 539-540.
Evans, B.E.; Rittle, K.E.; Bock, M.G.; DiPardo, R.M.; Freidinger,
R.M.; Whitter, W.L.; Lundell, G.F.; Veber, D.F.; Anderson, P.S.;
Chang, R.S.L.; Lotti, V.J.; Cerino, D.J.; Chen T.B.; Kling, P.J.;
Kunkel, K.A.; Springer, J.P.; Hirshfield, J. Methods for drug discovery: Development of potent, selective, orally effective cholecystokinin antagonists. J. Med. Chem., 1988, 31(12), 2235-2246.
Duarte, C.D.; Barreiro, E.J.; Fraga, C.A.M. Privileged structures: A
useful concept for the rational design of new lead drug candidates.
Mini Rev. Med. Chem., 2007, 7(11), 1108-1119.
Liu, Z.; Chen, X.; Yu, L.; Zhen, X.; Zhang, A. Synthesis and pharmacological investigation of novel 2-aminothiazole-privileged
aporphines. Bioorg. Med. Chem., 2008, 16(14), 6675-6681.
Computer-Aided Drug Design of Bioactive Natural Products
[53]
[54]
[55]
[56]
[57]
[58]
[59]
[60]
[61]
[62]
[63]
[64]
[65]
[66]
[67]
[68]
[69]
[70]
[71]
[72]
[73]
Ventosa-Andrés, P.; Hradilová, L.; Krchňák, V. Privileged structures as peptide backbone constraints: Polymer-supported stereoselective synthesis of benzimidazolinopiperazinone peptides. ACS
Comb. Sci., 2014, 16(7), 359-366.
Yu, B.; Yu, D.Q.; Liu, H.M. Spirooxindoles: Promising scaffolds
for anticancer agents. Eur. J. Med. Chem., In Press.
Welsch, M.E.; Snyder, S.A.; Stockwell, B.R. Privileged scaffolds
for library design and drug discovery. Curr. Opin. Chem. Biol.,
2010, 14(3), 347-361.
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and
permeability in drug discovery and development settings. Adv.
Drug Deliv. Rev., 1997, 23(1-3), 3-25.
van de Waterbeemd, H.; Smith, D.A.; Beaumont, K.; Walker, D.K.
Property-based design: Optimization of drug absorption and pharmacokinetics. J. Med. Chem., 2001, 44(9), 1313-1333.
Leeson, P.D.; Springthorpe, B. The influence of drug-like concepts
on decision-making in medicinal chemistry. Nat. Rev. Drug Discov., 2007, 6(11), 881-890.
Kola, I.; Landis, J. Can the pharmaceutical industry reduce attrition
rates? Nat. Rev. Drug Discov., 2004, 3(8), 711-715.
Merlot, C. Computational toxicology-a tool for early safety evaluation. Drug Discov. Today, 2010, 15(1-2), 16-22.
Li, A.P. Screening for human ADME/Tox drug properties in drug
discovery. Drug Discov. Today, 2001, 6(7), 357-366.
Grover, J.; Kumar, V.; Singh, V.; Bairwa, K.; Sobhia, M.E.;
Jachak, S.M. Synthesis, biological evaluation, molecular docking
and theoretical evaluation of ADMET properties of nepodin and
chrysophanol derivatives as potential cyclooxygenase (COX-1,
COX-2) inhibitors. Eur. J. Med. Chem., 2014, 80, 47-56.
Gupta, N.; Sitwala, N.; Patel, K. Pharmacophore modelling, validation, 3D virtual screening, docking, design and in silico ADMET
simulation study of histone deacetylase class-1 inhibitors. Med.
Chem. Res., 2014.
Kartasasmita, R.E.; Musfiroh, I.; Muhtadi, A.; Ibrahim, S. Binding
affinity of asiatic acid derivatives design against inducible nitric
oxide synthase and ADMET prediction. J. App. Pharm. Sci., 2014,
4(2), 75-80.
Li, D.; Chen, L.; Li, Y.; Tian, S.; Sun, H.; Hou, T. ADMET evaluation in drug discovery. 13. Development of in silico prediction
models for p-glycoprotein substrates. Mol. Pharm., 2014, 11(3),
716-726.
Rashid, U.; Hassan, S.F.; Nazir, S.; Wadood, A.; Waseem, M.;
Ansari, F.L. Synthesis, docking studies, and in silico ADMET predictions of some new derivatives of pyrimidine as potential KSP
inhibitors. Med. Chem. Res., 2014.
Vardhini, S.R.D. In silico evaluation for the potential naturally
available drugs for breast cancer. J. Recept. Signal Transduct. Res.,
2014, 34(3), 174-179.
Tetko, I.V. Computing chemistry on the web. Drug Discov. Today,
2005, 10(22), 1497-1500.
Irwin, J.J.; Shoichet, B.K. ZINC − A free database of commercially
available compounds for virtual screening. J. Chem. Inf. Model.,
2004, 45(1), 177-182.
Gaulton, A.; Bellis, L.J.; Bento, A.P.; Chambers, J.; Davies, M.;
Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; AlLazikani, B.; Overington, J.P. ChEMBL: a large-scale bioactivity
database for drug discovery. Nucleic Acids Res., 2012, 40(D1),
D1100-D1107.
Wang, Y.; Xiao, J.; Suzek, T.O.; Zhang, J.; Wang, J.; Bryant, S.H.
PubChem: a public information system for analyzing bioactivities
of small molecules. Nucleic Acids Res., 2009, 37(suppl 2), W623W633.
Aguero, F.; Al-Lazikani, B.; Aslett, M.; Berriman, M.; Buckner,
F.S.; Campbell, R.K.; Carmona, S.; Carruthers, I.M.; Chan,
A.W.E.; Chen, F.; Crowther, G.J.; Doyle, M.A.; Hertz-Fowler, C.;
Hopkins, A.L.; McAllister, G.; Nwaka, S.; Overington, J.P.; Pain,
A.; Paolini, G.V.; Pieper, U.; Ralph, S.A.; Riechers, A.; Roos, D.S.;
Sali, A.; Shanmugam, D.; Suzuki, T.; van Voorhis, W.C.; Verlinde,
C.L.M.J. Genomic-scale prioritization of drug targets: the TDR
Targets database. Nat. Rev. Drug Discov., 2008, 7(11), 900-907.
Gao, Z.; Li, H.; Zhang, H.; Liu, X.; Kang, L.; Luo, X.; Zhu, W.;
Chen, K.; Wang, X.; Jiang, H. PDTD: a web-accessible protein database for drug target identification. BMC Bioinformatics, 2008,
9(1), 104.
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
[74]
[75]
[76]
[77]
[78]
[79]
[80]
[81]
[82]
[83]
[84]
[85]
[86]
[87]
[88]
[89]
[90]
[91]
[92]
[93]
[94]
[95]
[96]
[97]
[98]
1797
Chen, X.; Ji, Z.L.; Chen, Y.Z. TTD: Therapeutic Target Database.
Nucleic Acids Res., 2002, 30(1), 412-415.
Rose, P.W.; Bi, C.; Bluhm, W.F.; Christie, C.H.; Dimitropoulos,
D.; Dutta, S.; Green, R.K.; Goodsell, D.S.; Prlić, A.; Quesada, M.;
Quinn, G.B.; Ramos, A.G.; Westbrook, J.D.; Young, J.; Zardecki,
C.; Berman;P.E. Bourne, H.M. The RCSB Protein Data Bank: new
resources for research and education. Nucleic Acids Res., 2013,
41(D1), D475-D482.
Vieth, M.; Siegel, M.G.; Higgs, R.E.; Watson, I.A.; Robertson,
D.H.; Savin, K.A.; Durst, G.L.; Hipskind, P.A. Characteristic physical properties and structural fragments of marketed oral drugs. J.
Med. Chem., 2003, 47(1), 224-232.
Ganesan, A. The impact of natural products upon modern drug
discovery. Curr. Opin. Chem. Biol., 2008, 12(3), 306-317.
Koch, M.A.; Schuffenhauer, A.; Scheck, M.; Wetzel, S.; Casaulta,
M.; Odermatt, A.; Ertl, P.; Waldmann, H. Charting biologically relevant chemical space: A structural classification of natural products
(SCONP). Proc. Natl. Acad. Sci. U.S.A., 2005, 102(48), 1727217277.
Reayi, A.; Arya, P. Natural product-like chemical space: search for
chemical dissectors of macromolecular interactions. Curr. Opin.
Chem. Biol., 2005, 9(3), 240-247.
Rosén, J.; Gottfries, J.; Muresan, S.; Backlund, A.; Oprea, T.I.
Novel chemical space exploration via natural products. J. Med.
Chem., 2009, 52(7), 1953-1962.
Osada, H.; Hertweck, C. Exploring the chemical space of microbial
natural products. Curr. Opin. Chem. Biol., 2009, 13(2), 133-134.
Lachance, H.; Wetzel, S.; Kumar, K.; Waldmann, H. Charting,
navigating, and populating natural product chemical space for drug
discovery. J. Med. Chem., 2012, 55(13), 5989-6001.
Zhou, X.; Li, Y.; Chen, X. Computational identification of bioactive natural products by structure activity relationship. J. Mol.
Graph. Model., 2010, 29(1), 38-45.
Wetzel, S.; Bon, R.S.; Kumar, K.; Waldmann, H. Biology-Oriented
Synthesis. Angew. Chem. Int. Ed. Engl., 2011, 50(46), 1080010826.
Newman, D.J.; Cragg, G.M. Natural products as sources of new
drugs over the last 25 years. J. Nat. Prod., 2007, 70(3), 461-477.
van Hattum, H.; Waldmann, H. Biology-oriented synthesis: harnessing the power of evolution. J. Am. Chem. Soc., 2014, 136(34),
11853-11859.
Newman, D.J.; Cragg, G.M. Natural products as sources of new
drugs over the 30 years from 1981 to 2010. J. Nat. Prod., 2012,
75(3), 311-335.
Hanada, M.; Sugawara, K.; Kaneta, K.; Toda, S.; Nishiyama, Y.;
Tomita, K.; Yamamoto, H.; Konishi, M.; Oki, T. Epoxomicin, a
new antitumor agent of microbial origin. J. Antibiot. (Tokyo), 1992,
45(11), 1746-1752.
Kim, K.B.; Myung, J.; Sin, N.; Crews, C.M. Proteasome inhibition
by the natural products epoxomicin and dihydroeponemycin: Insights into specificity and potency. Bioorg. Med. Chem. Lett., 1999,
9(23), 3335-3340.
Sin, N.; Kim, K.B.; Elofsson, M.; Meng, L.; Auth, H.; Kwok,
B.H.B.; Crews, C.M. Total synthesis of the-potent proteasome inhibitor epoxomicin: a useful tool for understanding proteasome biology. Bioorg. Med. Chem. Lett., 1999, 9(15), 2283-2288.
Newman, D.; Giddings, L.A. Natural products as leads to antitumor
drugs. Phytochem. Rev., 2014, 13(1), 123-137.
Group, C.R.C. Cephalotaxine esters in the treatment of acute leukemia. A preliminary clinical assessment. Chin. Med. J., 1976, 2,
263-272.
Fox, E.J. Mechanism of action of mitoxantrone. Neurology, 2004,
63(12 Suppl 6), S15-8.
Hande, K.R. Etoposide: four decades of development of a topoisomerase II inhibitor. Eur. J. Cancer, 1998, 34(10), 1514-1521.
Wender, P.A.; Verma, V.A.; Paxton, T.J.; Pillow, T.H. Functionoriented synthesis, step economy, and drug design. Acc. Chem.
Res., 2007, 41(1), 40-49.
Rizzo, S.; Waldmann, H. Development of a Natural-ProductDerived Chemical Toolbox for Modulation of Protein Function.
Chem. Rev., 2014, 114(9), 4621-4639.
Wach, J.Y.; Gademann, K. Reduce to the maximum: truncated
natural products as powerful modulators of biological processes.
Synlett, 2012, 2012(02), 163-170.
Surakanti, R.; Sanivarapu, S.; Thulluri, C.; Iyer, P.S.; Tangirala,
R.S.; Gundla, R.; Addepally, U.; Murthy, Y.L.N.; Velide, L.; Sen,
1798
[99]
[100]
[101]
[102]
[103]
[104]
[105]
[106]
[107]
[108]
[109]
[110]
[111]
[112]
[113]
[114]
[115]
[116]
[117]
[118]
[119]
[120]
[121]
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
S. Synthesis of privileged scaffolds by using diversity-oriented synthesis. Chem. Asian J., 2013, 8(6), 1168-1176.
Lewis, J.; Bentley, K.; Cowan, A. Narcotic analgesics and antagonists. Annu. Rev. Pharmacol., 1971, 11(1), 241-270.
Jain, K.S.; Kathiravan, M.K.; Somani, R.S.; Shishoo, C.J. The
biology and chemistry of hyperlipidemia. Bioorg. Med. Chem.,
2007, 15(14), 4674-4699.
Burke, M.D.; Schreiber, S.L. A planning strategy for diversityoriented synthesis. Angew. Chem. Int. Ed. Engl., 2004, 43(1), 4658.
Barjau, J.; Schnakenburg, G.; Waldvogel, S.R. Diversity-oriented
synthesis of polycyclic scaffolds by modification of an anodic
product derived from 2,4-dimethylphenol. Angew. Chem. Int. Ed.
Engl., 2011, 50(6), 1415-1419.
Sen, S.; Prabhu, G.; Bathula, C.; Hati, S. Diversity-oriented asymmetric synthesis. Synthesis, 2014, 46(16), 2099-2121.
Welsch, M.E.; Snyder, S.A.; Stockwell, B.R. Privileged scaffolds
for library design and drug discovery. Curr. Opin. Chem. Biol.,
2010, 14(3), 347-361.
Mackay, H.J.; Twelves, C.J. Protein kinase C: a target for anticancer drugs? Endocr. Relat. Cancer, 2003, 10(3), 389-96.
Manning, G.; Whyte, D.B.; Martinez, R.; Hunter, T.; Sudarsanam,
S. The protein kinase complement of the human genome. Science,
2002, 298(5600), 1912-1934.
Walensky, L.D.; Kung, A.L.; Escher, I.; Malia, T.J.; Barbuto, S.;
Wright, R.D.; Wagner, G.; Verdine, G.L.; Korsmeyer, S.J. Activation of apoptosis in vivo by a hydrocarbon-stapled BH3 helix. Science, 2004, 305(5689), 1466-1470.
Marquez, V.E.; Blumberg, P.M. Synthetic diacylglycerols (DAG)
and DAG-Lactones as activators of protein kinase C (PK-C). Acc.
Chem. Res., 2003, 36(6), 434-443.
Bon, R.S.; Waldmann, H. Bioactivity-guided navigation of chemical space. Acc. Chem. Res., 2010, 43(8), 1103-1114.
Doi, T.; Hoshina, Y.; Mogi, H.; Yamada, Y.; Takahashi, T. Solidphase combinatorial synthesis of aeruginosin derivatives and their
biological evaluation. J. Comb. Chem., 2006, 8(4), 571-582.
Nandy, J.P.; Prakesch, M.; Khadem, S.; Reddy, P.T.; Sharma, U.;
Arya, P. Advances in solution- and solid-phase synthesis toward
the generation of natural product-like libraries. Chem. Rev., 2009,
109(5), 1999-2060.
Marfil, M.; Albericio, F.; Álvarez, M. Solid-phase synthesis of
lamellarins Q and O. Tetrahedron, 2004, 60(39), 8659-8668.
Rivera, D.G.; Vercillo, O.E.; Wessjohann, L.A. Rapid generation
of macrocycles with natural-product-like side chains by multiple
multicomponent macrocyclizations (MiBs). Org. Biomol. Chem.,
2008, 6(10), 1787-1795.
Tanaka, N.; Okasaka, M.; Ishimaru, Y.; Takaishi, Y.; Sato, M.;
Okamoto, M.; Oshikawa, T.; Ahmed, S.U.; Consentino, L.M.; Lee,
K.H. Biyouyanagin A, an anti-HIV agent from Hypericum
chinense L. var. salicifolium. Org. Lett., 2005, 7(14), 2997-2999.
Wong, F.T.; Khosla, C. Combinatorial biosynthesis of polyketidesa perspective. Curr. Opin. Chem. Biol., 2012, 16(1–2), 117-123.
Yoon, Y.J.; Beck, B.J.; Kim, B.S.; Kang, H.Y.; Reynolds, K.A.;
Sherman, D.H. Generation of multiple bioactive macrolides by hybrid modular polyketide synthases in Streptomyces venezuelae.
Chem. Biol., 9(2), 203-214.
Sandmann, G. Combinatorial biosynthesis of carotenoids in a heterologous host: a powerful approach for the biosynthesis of novel
structures. ChemBioChem, 2002, 3(7), 629-635.
Cobb, R.; Ning, J.; Zhao, H. DNA assembly techniques for nextgeneration combinatorial biosynthesis of natural products. J. Ind.
Microbiol. Biotechnol., 2014, 41(2), 469-477.
Zhang, Y.; Luo, T.; Yang, Z. Strategic innovation in the total synthesis of complex natural products using gold catalysis. Nat. Prod.
Rep., 2014, 31(4), 489-503.
Corrêa, I.R.; Nören-Müller, A.; Ambrosi, H.D.; Jakupovic, S.;
Saxena, K.; Schwalbe, H.; Kaiser, M.; Waldmann, H. Identification
of inhibitors for Mycobacterial Protein Tyrosine Phosphatase B
(MptpB) by biology-oriented synthesis (BIOS). Chem. Asian J.,
2007, 2(9), 1109-1126.
Nören-Müller, A.; Reis-Corrêa, I.; Prinz, H.; Rosenbaum, C.;
Saxena, K.; Schwalbe, H.J.; Vestweber, D.; Cagna, G.; Schunk, S.;
Schwarz, O.; Schiewe, H.; Waldmann, H. Discovery of protein
phosphatase inhibitor classes by biology-oriented synthesis. Proc.
Natl. Acad. Sci. U. S. A., 2006, 103(28), 10606-10611.
Prachayasittikul et al.
[122]
[123]
[124]
[125]
[126]
[127]
[128]
[129]
[130]
[131]
[132]
[133]
[134]
[135]
[136]
[137]
[138]
[139]
[140]
[141]
Hansch, C.; Fujita, T. p-σ-π Analysis. A method for the correlation
of biological activity and chemical structure. J. Am. Chem. Soc.
1964, 86(8), 1616-1626.
Hansch, C. Quantitative approach to biochemical structure-activity
relationships. Acc. Chem. Res., 1969, 2(8), 232-239.
van Westen, G.J.; Wegner, J.K.; IJzerman, A.P.; van Vlijmen,
H.W.; Bender, A. Proteochemometric modeling as a tool to design
selective compounds and for extrapolating to novel targets. MedChemComm, 2011, 2(1), 16-30.
Nantasenamat, C.; Isarankura-Na-Ayudhya, C.; Prachayasittikul,
V. Advances in computational methods to predict the biological activity of compounds. Expert Opin. Drug Discov., 2010, 5(7), 633654.
Geldenhuys, W.J.; Gaasch, K.E.; Watson, M.; Allen, D.D.; van der
Schyf, C.J. Optimizing the use of open-source software applications in drug discovery. Drug Discov. Today, 2006, 11(3–4), 127132.
Nantasenamat, C.; Li, H.; Mandi, P.; Worachartcheewan, A.; Monnor, T.; Isarankura-Na-Ayudhya, C.; Prachayasittikul, V. Exploring
the chemical space of aromatase inhibitors. Mol. Divers., 2013,
17(4), 661-677.
Nabu, S.; Nantasenamat, C.; Owasirikul, W.; Lawung, R.;
Isarankura-Na-Ayudhya, C.; Lapins, M.; Wikberg, J.E.S.; Prachayasittikul, V. Proteochemometric model for predicting the inhibition of penicillin-binding proteins. J.Comput. Aided Mol. Des.,
2014.
Nantasenamat, C.; Monnor, T.; Worachartcheewan, A.; Mandi, P.;
Isarankura-Na-Ayudhya, C.; Prachayasittikul, V. Predictive QSAR
modeling of aldose reductase inhibitors using Monte Carlo feature
selection. Eur. J. Med. Chem., 2014, 76, 352-359.
Nantasenamat, C.; Worachartcheewan, A.; Mandi, P.; Monnor, T.;
Isarankura-Na-Ayudhya, C.; Prachayasittikul, V. QSAR modeling
of aromatase inhibition by flavonoids using machine learning approaches. Chem. Papers, 2014, 68(5), 697-713.
Pingaew, R.; Mandi, P.; Nantasenamat, C.; Prachayasittikul, S.;
Ruchirawat, S.; Prachayasittikul, V. Design, synthesis and molecular docking studies of novel N-benzenesulfonyl-1,2,3,4tetrahydroisoquinoline-based triazoles with potential anticancer activity. Eur. J. Med.Chem., 2014, 81, 192-203.
Pingaew, R.; Saekee, A.; Mandi, P.; Nantasenamat, C.; Prachayasittikul, S.; Ruchirawat, S.; Prachayasittikul, V. Synthesis, biological evaluation and molecular docking of novel chalcone-coumarin
hybrids as anticancer and antimalarial agents. Eur. J. Med. Chem.,
2014, 85, 65-76.
Prachayasittikul, V.; Pingaew, R.; Worachartcheewan, A.;
Nantasenamat, C.; Prachayasittikul, S.; Ruchirawat, S.; Prachayasittikul, V. Synthesis, anticancer activity and QSAR study of 1,4naphthoquinone derivatives. Eur. J. Med. Chem., 2014, 84, 247263.
Worachartcheewan, A.; Nantasenamat, C.; Isarankura-NaAyudhya, C.; Prachayasittikul, V. QSAR study of H1N1 neuraminidase inhibitors from influenza a virus. Lett. Drug Design Discov., 2014, 11(4), 420-427.
Worachartcheewan, A.; Nantasenamat, C.; Owasirikul, W.; Monnor, T.; Naruepantawart, O.; Janyapaisarn, S.; Prachayasittikul, S.;
Prachayasittikul, V. Insights into antioxidant activity of 1adamantylthiopyridine analogs using multiple linear regression.
Eur. J. Med. Chem., 2014, 73, 258-264.
Dudek, A.Z.; Arodz, T.; Galvez, J. Computational methods in
developing quantitative structure-activity relationships (QSAR): a
review. Comb. Chem. High Throughput Screen., 2006, 9(3), 21328.
Labute, P. A widely applicable set of descriptors. J. Mol. Graph.
Model., 2000, 18(4–5), 464-477.
Nikolova, N.; Jaworska, J. Approaches to measure chemical similarity – a review. QSAR Comb. Sci., 2003, 22(9-10), 1006-1026.
Consonni, V.; Todeschini, R.; Pavan, M. Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors.
1. Theory of the novel 3D molecular descriptors. J. Chem. Inf.
Comput. Sci., 2002, 42(3), 682-692.
Xue, L.; Bajorath, J. Molecular descriptors in chemoinformatics,
computational combinatorial chemistry, and virtual screening.
Comb. Chem. High Throughput Screen., 2000, 3(5), 363-372.
Tetko, I.; Gasteiger, J.; Todeschini, R.; Mauri, A.; Livingstone, D.;
Ertl, P.; Palyulin, V.; Radchenko, E.; Zefirov, N.; Makarenko, A.;
Tanchuk, V.; Prokopenko, V. Virtual computational chemistry la-
Computer-Aided Drug Design of Bioactive Natural Products
[142]
[143]
[144]
[145]
[146]
[147]
[148]
[149]
[150]
[151]
[152]
[153]
[154]
[155]
[156]
[157]
[158]
[159]
[160]
[161]
[162]
[163]
[164]
[165]
[166]
[167]
[168]
[169]
[170]
[171]
boratory – design and description. J. Comput. Aided Mol. Des.,
2005, 19(6), 453-463.
Hinselmann, G.; Rosenbaum, L.; Jahn, A.; Fechner, N.; Zell, A.
jCompoundMapper: An open source Java library and commandline tool for chemical fingerprints. J. Cheminform., 2011, 3(1), 3.
Saeys, Y.; Inza, I.; Larrañaga, P. A review of feature selection
techniques in bioinformatics. Bioinformatics, 2007, 23(19), 25072517.
Zhang, C.; Zhang, S. Association rule mining: models and algorithms; Springer-Verlag, 2002.
Pudil, P.; Novovičová, J.; Kittler, J. Floating search methods in
feature selection. Pattern Recognit. Lett., 1994, 15(11), 1119-1125.
Davis, L. Handbook of genetic algorithms. Vol. 115; Van Nostrand
Reinhold: New York, 1991.
Quinlan, J.R. C4. 5: programs for machine learning. Vol. 1; Morgan kaufmann: CA, 1993.
Landwehr, N.; Hall, M.; Frank, E. Logistic model trees, In: Machine Learning: ECML 2003; Springer, 2003; pp. 241-252.
Breiman, L. Random forests. Mach. Learn., 2001, 45(1), 5-32.
Jolliffe, I. Principal component analysis; Wiley Online Library,
2005.
Geladi, P.; Kowalski, B.R. Partial least-squares regression: a tutorial. Anal. Chim. Acta., 1986, 185, 1-17.
Tenenhaus, M. La régression PLS: théorie et pratique; Editions
Technip, 1998.
Helland, I.S. Some theoretical aspects of partial least squares regression. Chemometr. Intell. Lab. Syst., 2001, 58(2), 97-107.
Rosenblatt, F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev., 1958,
65(6), 386.
Anderson, J.R.; Michalski, R.S.; Carbonell, J.G.; Mitchell, T.M.
Machine learning: An artificial intelligence approach. Vol. 2;
Morgan Kaufmann, 1986.
Russell, S.; Norvig, P. Artificial Intelligence : A Modern Approach,
1st ed.; Prentice-Hall: Egnlewood Cliffs, 1995, 25.
Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn.,
1995, 20(3), 273-297.
Platt, J. Sequential minimal optimization: A fast algorithm for
training support vector machines, 1998.
Quinlan, J.R. Simplifying decision trees. Int. J. Man Mach. Stud.,
1987, 27(3), 221-234.
Hall, M.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.;
Witten, I.H. The WEKA data mining software: an update. SIGKDD
Explor. Newsl., 2009, 11(1), 10-18.
Shoombuatong, W.; Hongjaisee, S.; Barin, F.; Chaijaruwanich, J.;
Samleerat, T. HIV-1 CRF01_AE coreceptor usage prediction using
kernel methods based logistic model trees. Comput. Biol. Med.,
2012.
Breiman, L.; Friedman, J.; Stone, C.J.; Olshen, R.A. Classification
and regression trees; CRC press, 1984.
Calle, M.L.; Urrea, V. Letter to the editor: Stability of random
forest importance measures. Brief. Bioinform., 2011, 12(1), 86-89.
Breiman, L.; Friedman, J.H.; Olshen, R.A.; Stone, C.J. Classification and regression trees; Wadsworth & Brooks: Monterey, CA,
1984.
Bishop, C.M., Pattern recognition and machine learning. Vol. 1;
Springer: New York, 2006.
Golbraikh, A.; Tropsha, A. Beware of q2! J. Mol. Graph. Model.,
2002, 20(4), 269-276.
Melagraki, G.; Afantitis, A. Enalos KNIME nodes: Exploring corrosion inhibition of steel in acidic medium. Chemometr. Intell. Lab.
Syst., 2013, 123(0), 9-14.
Zhang, S.; Golbraikh, A.; Oloff, S.; Kohn, H.; Tropsha, A. A novel
automated lazy learning QSAR (ALL-QSAR) approach: method
development, applications, and virtual screening of chemical databases using validated ALL-QSAR models. J. Chem. Inf. Model.,
2006, 46(5), 1984-1995.
Refaeilzadeh, P.; Tang, L.; Liu, H. Cross-validation, In: Encyclopedia of database systems; Springer, 2009; pp. 532-538.
Xu, J.; Wang, L.; Wang, L.; Shen, X.; Xu, W. QSPR study of
Setschenow constants of organic compounds using MLR, ANN,
and SVM analyses. J. Comput. Chem., 2011, 32(15), 3241-3252.
Melagraki, G.; Afantitis, A. Enalos InSilicoNano platform: an
online decision support tool for the design and virtual screening of
nanoparticles. RSC Adv., 2014, 4(92), 50713-50725.
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
[172]
[173]
[174]
[175]
[176]
[177]
[178]
[179]
[180]
[181]
[182]
[183]
[184]
[185]
[186]
[187]
[188]
[189]
[190]
[191]
[192]
[193]
1799
Hinde, J.; Demétrio, C.G. Overdispersion: models and estimation.
Comput. Stat. Data Anal., 1998, 27(2), 151-170.
Gaurav, A.; Gautam, V.; Pereira, S.; Alvarez-Leite, J.; Vetri, F.;
Choudhury, M.; Pelligrino, D.; Sundivakkam, P.; Radhakrishnan,
K.; Krieger, A. Structure-based three-dimensional pharmacophores
as an alternative to traditional methodologies. J. Receptor Ligand
Channel Res., 2014, 7, 27-38.
Guner, O.F. History and evolution of the pharmacophore concept in
computer-aided drug design. Curr. Top. Med. Chem., 2002, 2(12),
1321-32.
Strebhardt, K.; Ullrich, A. Paul Ehrlich's magic bullet concept: 100
years of progress. Nat. Rev. Cancer, 2008, 8(6), 473-480.
Kurogi, Y.; Guner, O.F. Pharmacophore modeling and threedimensional database searching for drug design using catalyst.
Curr. Med. Chem., 2001, 8(9), 1035-55.
Langer, T.; Krovat, E.M. Chemical feature-based pharmacophores
and virtual library screening for discovery of new leads. Curr.
Opin. Drug Discov. Devel., 2003, 6(3), 370-6.
Franke, L.; Byvatov, E.; Werz, O.; Steinhilber, D.; Schneider, P.;
Schneider, G. Extraction and visualization of potential pharmacophore points using support vector machines: application to ligandbased virtual screening for COX-2 inhibitors. J. Med. Chem., 2005,
48(22), 6997-7004.
Leach, A.R.; Gillet, V.J.; Lewis, R.A.; Taylor, R. Threedimensional pharmacophore methods in drug discovery. J. Med.
Chem., 2009, 53(2), 539-558.
Guner, O.F. Pharmacophore perception, development, and use in
drug design; International University Line: La Jolla, CA., 2000.
Langer, T.; Hoffmann, R.D. Pharmacophore modelling: applications in drug discovery. Expert Opin. Drug Discov., 2006, 1(3),
261-7.
Sanders, M.P.A.; McGuire, R.; Roumen, L.; de Esch, I.J.P.; de
Vlieg, J.; Klomp, J.P.G.; de Graaf, C. From the protein's perspective: the benefits and challenges of protein structure-based pharmacophore modeling. MedChemComm, 2012, 3(1), 28-38.
Yang, S.Y. Pharmacophore modeling and applications in drug
discovery: challenges and recent advances. Drug Discov. Today,
2010, 15(11–12), 444-450.
Cramer, R.D.; Patterson, D.E.; Bunce, J.D. Comparative molecular
field analysis (CoMFA). 1. Effect of shape on binding of steroids to
carrier proteins. J. Am. Chem. Soc., 1988, 110(18), 5959-5967.
Chin, W.W. The partial least squares approach to structural equation modeling, In: Modern methods for business research; Marcoulides, G.A., Ed.; Lawrence Erlbaum Associates Publishers: Mahwah, NJ, US, 1998; pp. 295-336.
Wolber, G.; Seidel, T.; Bendix, F.; Langer, T. Moleculepharmacophore superpositioning and pattern matching in computational drug design. Drug Discov. Today, 2008, 13(1–2), 23-29.
Martin, Y.; Bures, M.; Danaher, E.; DeLazzer, J.; Lico, I.; Pavlik,
P. A fast new approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists. J. Comput. Aided Mol. Des., 1993, 7(1), 83-102.
Chang, C.; Swaan, P.W.; Ngo, L.Y.; Lum, P.Y.; Patil, S.D.; Unadkat, J.D. Molecular requirements of the human nucleoside transporters hCNT1, hCNT2, and hENT1. Mol. Pharm., 2004, 65(3),
558-570.
Le Bourdonnec, B.; Cauvin, C.; Meulon, E.; Yous, S.; Goossens,
J.F.; Durant, F.; Houssin, R.; Hénichart, J.P. Comparison of 3D
structures and AT1 binding properties of pyrazolidine-3,5-diones
and tetrahydropyridazine-3,6-diones with parent antihypertensive
drug irbesartan. J. Med. Chem., 2002, 45(21), 4794-4798.
Fossa, P.; Boggia, R.; Lo Presti, E.; Mosti, L.; Dorigo, P.; Floreani,
M. Inotropic agents. Synthesis and structure-activity relationships
of new milrinone related cAMP PDE III inhibitors. Farmaco, 1997,
52(8-9), 523-30.
Nicolotti, O.; Pellegrini-Calace, M.; Carrieri, A.; Altomare, C.;
Centeno, N.B.; Sanz, F.; Carotti, A. Neuronal nicotinic receptor
agonists: a multi-approach development of the pharmacophore. J.
Comput. Aided Mol. Des., 2001, 15(9), 859-872.
Schuster, I.; Egger, H.; Nussbaumer, P.; Kroemer, R.T. Inhibitors
of vitamin D hydroxylases: Structure–activity relationships. J. Cell
Biochem., 2003, 88(2), 372-380.
Fossa, P.; Boggia, R.; Mosti, L. Toward the identification of the
cardiac cGMP inhibited-phosphodiesterase catalytic site. J. Comput. Aided Mol. Des., 1998, 12(4), 361-361.
1800
[194]
[195]
[196]
[197]
[198]
[199]
[200]
[201]
[202]
[203]
[204]
[205]
[206]
[207]
[208]
[209]
[210]
[211]
[212]
[213]
[214]
Current Topics in Medicinal Chemistry, 2015, Vol. 15, No. 18
Jones, G.; Willett, P.; Glen, R.C. GASP: genetic algorithm superimposition program, In: Pharmacophore Perception, Development,
and Use in Drug Design; International University Line: La Jolla,
CA, USA, 2000; pp. 85-106.
Barnum, D.; Greene, J.; Smellie, A.; Sprague, P. Identification of
common functional configurations among molecules. J. Chem. Inf.
Comput. Sci., 1996, 36(3), 563-571.
Li, H.; Sutter, J.; Hoffmann, R. HypoGen: an automated system for
generating 3D predictive pharmacophore models, In: Pharmacophore Perception, Development, and Use in Drug Design;
International University Line: La Jolla, CA, USA, 2000; pp. 171.
Chemical Computing Group. Molecular Operating Environment
(MOE) v2006.08; Chemical Computing Group: Montreal, Quebec,
Canada, 2006.
Dixon, S.; Smondyrev, A.; Knoll, E.; Rao, S.; Shaw, D.; Friesner,
R. PHASE: a new engine for pharmacophore perception, 3D QSAR
model development, and 3D database screening: 1. Methodology
and preliminary results. J. Comput. Aided Mol. Des., 2006, 20(1011), 647-671.
Richmond, N.J.; Abrams, C.A.; Wolohan, P.R.; Abrahamian, E.;
Willett, P.; Clark, R.D. GALAHAD: 1. Pharmacophore identification by hypermolecular alignment of ligands in 3D. J. Comput.
Aided Mol. Des., 2006, 20(9), 567-587.
Wolber, G.; Langer, T. LigandScout: 3D Pharmacophores derived
from protein-bound ligands and their use as virtual screening filters. J. Chem. Inf. Model., 2004, 45(1), 160-169.
Alonso, H.; Bliznyuk, A.A.; Gready, J.E. Combining docking and
molecular dynamic simulations in drug design. Med. Res. Rev.,
2006, 26(5), 531-568.
Halperin, I.; Ma, B.; Wolfson, H.; Nussinov, R. Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins Struct. Funct. Genet., 2002, 47(4), 409-443.
Brooijmans, N.; Kuntz, I.D. Molecular recognition and docking
algorithms. Annu. Rev. Biophys. Biomol. Struct., 2003, 32(1), 335373.
Kitchen, D.B.; Decornez, H.; Furr, J.R.; Bajorath, J. Docking and
scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov., 2004, 3(11), 935-949.
Li, J. Basic molecular dynamics, In: Handbook of Materials Modeling; Springer, 2005, pp. 565-588.
Adcock, S.A.; McCammon, J.A. Molecular dynamics: survey of
methods for simulating the activity of proteins. Chem. Rev., 2006,
106(5), 1589-1615.
Galeazzi, R. Molecular dynamics as a tool in rational drug design:
current status and some major applications. Curr. Comput. Aided
Drug Des., 2009, 5(4), 225-240.
Kortagere, S.; Lill, M.; Kerrigan, J. Role of computational methods
in pharmaceutical sciences, In: Computational Toxicology; Springer, 2012; pp. 21-48.
Ian, I.F.; Luis, R.T.J.; Pablo, C.V.J.; Luis, V.S.J.; Normande, C.I.;
Beatriz, Z.L.; Sandino, R.L.C.A.; Guadalupe, B.C.C.; Jose, C.B.;
Absalom, Z.C. Identification of pharmacological targets combining
docking and molecular dynamics simulations. Am. J. Agric. Biol.
Sci., 2013, 8(1), 89.
Ma, D.L.; Chan, D.S.H.; Leung, C.H. Molecular docking for virtual
screening of natural product databases. Chem. Sci., 2011, 2(9),
1656-1665.
Medina-Franco, J.L.; López-Vallejo, F.; Kuck, D.; Lyko, F. Natural
products as DNA methyltransferase inhibitors: a computer-aided
discovery approach. Mol. Divers., 2011, 15(2), 293-304.
Du, H.; Wang, J.; Watzl, J.; Zhang, X.; Hu, Z. Classification structure–activity relationship (CSAR) studies for prediction of genotoxicity of thiophene derivatives. Toxicol. Lett., 2008, 177(1), 1019.
Ma, S.K.; Wu, K.Z.; Li, A.X. Virtual screening for natural product
inhibitors of HIV-1 integrase. Interdiscip. Sci., 2011, 3(1), 17-21.
Lemkul, J.A.; Bevan, D.R. Morin inhibits the early stages of amyloid beta-peptide aggregation by altering tertiary and quaternary in-
Received: December 05, 2014
Revised: February 19, 2015
Accepted: February 20, 2015
Prachayasittikul et al.
[215]
[216]
[217]
[218]
[219]
[220]
[221]
[222]
[223]
[224]
[225]
[226]
[227]
[228]
[229]
[230]
[231]
[232]
[233]
[234]
[235]
teractions to produce "off-pathway" structures. Biochemistry, 2012,
51(30), 5990-6009.
Hou, D.X.; Kumamoto, T. Flavonoids as protein kinase inhibitors
for cancer chemoprevention: direct binding and molecular modeling. Antioxid. Redox. Signal., 2010, 13(5), 691-719.
Chen, C.Y.C. TCM Database@Taiwan: the world's largest traditional Chinese medicine database for drug screening in silico. PLoS
One, 2011, 6(1), e15939.
Sun, M.F.; Yang, S.C.; Chang, K.W.; Tsai, T.Y.; Chen, H.Y.; Tsai,
F.J.; Lin, J.G.; Chen, C.Y.C. Screening from TCM Database@
Taiwan and QSAR model for identifying HER2 inhibitors. Mol.
Simul., 2011, 37(10), 884-892.
Yang, S.C.; Chang, S.S.; Chen, H.Y.; Chen, C.Y.C. Identification
of potent EGFR inhibitors from TCM Database@Taiwan. PLoS
Comput. Biol., 2011, 7(10), e1002189.
Yang, S.C.; Chang, S.S.; Chen, C.Y.C. Identifying HER2 inhibitors
from natural products database. PLoS One, 2011, 6(12), e28793.
Khuntawee, W.; Rungrotmongkol, T.; Hannongbua, S. Molecular
dynamic behavior and binding affinity of flavonoid analogues to
the cyclin dependent kinase 6/cyclin D complex. J. Chem. Inf.
Model., 2011, 52(1), 76-83.
Yim-Im, W.; Sawatdichaikul, O.; Semsri, S.; Horata, N.; Mokmak,
W.; Tongsima, S.; Suksamrarn, A.; Choowongkomon, K. Computational analyses of curcuminoid analogs against kinase domain of
HER2. BMC Bioinformatics, 2014, 15(1), 1-13.
Ngo, S.T.; Li, M.S. Curcumin binds to Abeta1-40 peptides and
fibrils stronger than ibuprofen and naproxen. J. Phys. Chem. B.,
2012, 116(34), 10165-75.
Ngo, S.T.; Li, M.S. Top-leads from natural products for treatment
of Alzheimer's disease: docking and molecular dynamics study.
Mol. Simul., 2013, 39(4), 279-291.
Lemkul, J.A.; Bevan, D.R. Destabilizing Alzheimer's Abeta(42)
protofibrils with morin: mechanistic insights from molecular dynamics simulations. Biochemistry, 2010, 49(18), 3935-46.
Lemkul, J.A.; Bevan, D.R. The role of molecular simulations in the
development of inhibitors of amyloid beta-peptide aggregation for
the treatment of Alzheimer's disease. ACS Chem. Neurosci., 2012,
3(11), 845-56.
Liu, M.Y.; Chen, K.J. Convergence: The tradition and the modern.
Chin. J. Integr. Med., 2012, 18(3), 164-165.
Eisenberg, D. Reflections on the past and future of integrative
medicine from a lifelong student of the integration of Chinese and
Western Medicine. Chin. J. Integr. Med., 2011, 17(1), 3-5.
Chaudhary, A.; Singh, N. Contribution of world health organization
in the global acceptance of Ayurveda. J. Ayurveda Integr. Med.,
2011, 2(4), 179.
Xu, H.; Chen, K.J. Complementary and alternative medicine: is it
possible to be mainstream? Chin. J. Integr. Med., 2012, 18(6), 403404.
Weil, A. The state of the integrative medicine in the US and Western World. Chin. J. Integr. Med., 2011, 17(1), 6-10.
Park, J.; Beckman-Harned, S.; Cho, G.; Kim, D.; Kim, H. The
current acceptance, accessibility and recognition of Chinese and
Ayurvedic medicine in the United States in the public, governmental, and industrial sectors. Chin. J. Integr. Med., 2012, 18(6), 405408.
Manek, N.; Crowson, C.; Ottenberg, A.; Curlin, F.; Kaptchuk, T.;
Tilburt, J. What rheumatologists in the United States think of complementary and alternative medicine: results of a national survey.
BMC Complement. Altern. Med., 2010, 10(1), 5.
Lu, W.I.; Lu, D.P. Impact of Chinese herbal medicine on American
society and health care system: perspective and concern. Evid.
Based Complement. Alternat. Med., 2014, 2014, 6.
David, B.; Wolfender, J.L.; Dias, D. The pharmaceutical industry
and natural products: historical status and new trends. Phytochem.
Rev., 2014, 1-17.
Sahoo, N.; Manchikanti, P.; Dey, S. Herbal drugs: Standards and
regulation. Fitoterapia, 2010, 81(6), 462-471.