Module I- Computer Aided Drug Designing

• Introduction to CADD and its approaches
• Databases (PubMed, UniProt, DrugBank, PubChem, RSCB-PDB, KEGG)
• File formats for CADD (sdf, mol, pdb)
• Target Identification (using literature mining approaches)
• Homology modelling of proteins (SWISS-model, I-Tasser)
• Protein Structure Validation (ProSA)
• Active site prediction (MetaPocket, CastP, PyMol)
• Molecular Docking (AutoDock Vina)
• Interpretation of docking results (Binding affinity, RMSD, Hydrogen Bonds)
• Visualization of protein-ligand complex (PyMol, Discovery Studio)
• Molecular Dynamic Simulation (GROMACS)

Module II- Drug designing using Machine Learning Approach

• Introduction to Machine learning in Drug Designing
• Data collection
• Target Identification (using literature mining approaches)
• Compound identification (ExPasy, Chembl)
• Model Generation, Validation and Inference (Python libraries