Back close

Drug Discovery from Medicinal Plants using Machine Learning Approaches

Project Incharge:Dr. Ani R.
Co-Project Incharge:Dr. Deepa Gopakumar O. S.
Co-Project Incharge:Dr. Teena Merlin, Asst. Professor, School of Biosciences, Mar Athanasious College for Advanced Studies, MG University
Drug Discovery from Medicinal Plants using Machine Learning Approaches

Computational tools are widely used nowadays to aid research studies on early stage of drug development for known and new diseases. Big Data analytics with high performance computing machines and advances in molecular modeling software has revolutionized the in-silico drug discovery process. This study integrates PPI analysis with in-silico virtual screening in Drug discovery. For a selected disease of interest, potential drugs need to be discovered. We study the complete proteome of the disease using PPIs and select the Drug target molecules in the proteome. Virtual screening techniques will be used to identify potential drug likeliness compounds. Then we use computational docking studies to identify drug candidates for the selected disease. This study integrates PPI analysis with in-silico virtual screening in Drug discovery. For a selected disease of interest, potential drugs need to be discovered. We study the complete proteome of the disease using PPIs and select the Drug target molecules in the proteome. Virtual screening techniques will be used to identify potential drug likeliness compounds. Then we use computational docking studies to identify drug candidates for the selected disease.

Related Projects

Grid Integration of Renewable Energy Sources with Power Quality Improvement
Grid Integration of Renewable Energy Sources with Power Quality Improvement
Fulbright Specialist Scholar Program
Fulbright Specialist Scholar Program
Image Registration using Differential Evolution Based Algorithm Portfolios
Image Registration using Differential Evolution Based Algorithm Portfolios
VALUE Virtual Simulation Labs
VALUE Virtual Simulation Labs
MySangham Online Portal
MySangham Online Portal
Admissions Apply Now