Polypharmacology analysis
Despite of enormous increase in chemical, biological and bioactivity data, there is diminution in development new drug molecules. Recent investigation shows that drug-target interactions are not as simple as 1:1 as previously thought. Profiling interactions of these entities are much needed to purpose or repurpose chemical substances towards therapeutic targets. Machine learning based approach are being used to identify and predict of drug interaction profile on genomic scale.
- Molecular BioSystems, 2016, 10.1039/C5MB00650C
- Molecular Diversity, 2013, 17 (1), 97-110
Membrane transporters
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Toxicity analysis
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Kinetoplastids
Trypanosomatids are a group of kinetoplastid protozoa which causes various tropical infectious diseases. In order to identify novel drug target for Leishmania donovani, we are developing a genome-scale metabolic model by tools and techniques of theoretical Systems Biology. By using cell based phenotypic screening data, we are also developing machine learning models for the prediction of therapeutic targets for T. cruzi, T. brucei and L. donovani. LeishBase is a structural database comprises of 347 homology models of various L. major proteins.
Protein function
Functional annotation of protein at genomic scale is essential for any systematic approach to the modeling of biological systems. Protein function prediction methods are techniques that bioinformaticians use to assign biochemical roles to proteins. By using various machine-learning approaches, we work on the development of predictive models for Enzyme Commission number (E.C. number), Sub-cellular localization (SubCellProt) and protein-protein interactions(ProIntPred).
- In Silico Biology, vol. 9, no. 1,2, pp. 35-44, 2009
- Current Proteomics, Volume 11, pp. 17-22(6), April 2014
ADME
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Last Updated on 29th Jan. 2014