Download MSc in Bioorganic Chemistry Dissertation Project – 2nd Cycle

MSc in Bioorganic Chemistry
Dissertation Project – 2nd Cycle
Student´s Name:
Student email address:
No.
Supervisor(s): Florbela Pereira/Susana P. Gaudêncio
Supervisor(s) email address:[email protected]/[email protected]
Lab/Institution: Dept. Química, FCT/UNL
Scientific area: Chemoinformatics
TITLE:Developing a computer-aided drug design approach to discovery lead-like antiinflammatory drugs
BACKGROUND
The success rate of drug discovery from the marine world is 1 drug per 3,140 natural products
described. This rate is approximately 1.7-to 3.3-fold better than the industry average (1 in 5,000–
10,000 tested compounds).
Chronic inflammatory-derived conditions are increasing, especially in Europe as the average
population ages. Consequently, the demand for the development of new drugs to treat and control
multi-morbidity conditions has increased.
Achemoinformatics approach heavily relying on ligand-based methodology using machine learning
techniques to predict by modelling the Quantitative Structure–Activity Relationships (QSAR) such as
anti-inflammatory can be very useful to prioritize the anti-inflammatory potential of marine natural
products (MNPs) in cell-based assays.
S. Gaudêncio’s team has already built a marine microbe library of 400actinomycete strains isolated
from the sediments from Madeira Archipelago, including bioactive pure compounds library.
OBJECTIVES
The main goals of this project are a) developing three QSAR models to predict phospholipase A2 (PLA2),
lipoxygenase (LOX) and NF-kB pathway anti-inflammatory activities using structure-derived molecular
descriptors, which encode properties such as physicochemical, topological, structural, spatial,
electronic and quantum chemical; b) virtual screening of the MNP library to identify promising MNPs
for the development of new lead-like anti-inflammatory drugs.
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MSc in Bioorganic Chemistry
Dissertation Project – 2nd Cycle
PROJECT DESCRIPTION
The relationship between structure and biological activities of NPs are very complex processes,which
many scientific studies have tried to explain. QSAR modeling can contribute in an important way to
fully understand the processes involved in anti-inflammatory activity. The expensive and timeconsuming approach that has been used to prioritize the discovery of new drugs in the MNPs field,
which consist in the screening of all the samples for anti-inflammatory activity, will be improved with
the present project proposal in a “QSAR prioritization", which will be able to predict the antiinflammatory activity of new samples through a virtual screening. Thus, the predicted antiinflammatory activity can be evaluated in a real screening, which is effectively translates in an
increased of the efficient as compared to the usual random screening process.
Task 1 -Building a database
Approximately 20,000 molecules will be extracted from ChEMBL, PubChem, Zinc and AntiMarin
databases and recent literature. For example, the following number of compounds with IC50 values
could be retrieved from the ChEMBL database in a preliminary exploration for the PLA2, LOX, and NFkB anti-inflammatory activities: 2188, 6297 and 6897 compounds, respectively. Empirical molecular
(EM) descriptors and Fingerprints will be calculated by PaDEL-Descriptor version 2.11. Fast estimation
of DFT properties, previously developed by machine Learning (ML) techniques in the Chemoinformatics
lab for bond energies, partial atomic charges and molecular orbital energies, will enable to include
quantum chemistry information with the required speed to process thousands of compounds in a few
seconds.
Task 2–QSAR modelling
Building of ML models for prediction of PLA2, LOXand NF-kB pathway inhibitory activities. These will
include state-of-the-art ML techniques such as random forests, multi-layer perceptrons and supportvector machines.
Task 3–Virtual screening
Application of the models developed in (task 2) for the virtual screening of the MNP library to identify
promising MNPs for the development of new lead-like anti-inflammatory drugs
TIMELINE(use fill tool for the cells)
Task 1
Task 2
Task 3
Thesis
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