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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. 1 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 Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Month 7 Month 8 Month 9 Month 10 x X x X X X x x x x x X X X 2