Fundamentals of Drug Discovery & Design

Week 1: Drug Discovery Basics + Target Identification

1. Day 01: Drug Discovery Pipeline – Target Identification, Hit Discovery, Lead Optimization, Preclinical and Clinical Development, FDA Approval, Time, Cost, and Attrition Rates
2. Day 02: Target Identification and Validation – Proteins, Enzymes, Receptors, Ion Channels, Omics, and Literature Mining
3. Day 03: Drug–Target Interactions – Binding Affinity, Kd, IC50, EC50, Agonists, Antagonists, Inhibitors, and Dose–Response Curves
4. Day 04: Introduction to Pharmacokinetics – ADME, Cmax, Tmax, Half-Life, and Bioavailability
5. Day 05: Introduction to Pharmacodynamics – Efficacy, Potency, Therapeutic Index, Therapeutic Window, and Receptor Theory

Week 2: Drug Classes + Cheminformatics Fundamentals

1. Day 06: Drug Classes and Examples – Small Molecules, Biologics, Peptides, Statins, and Imatinib
2. Day 07: Hands-on: Drug Profiling Using DrugBank – Metformin, Aspirin, and Atorvastatin
3. Day 08: Chemical Representation – SMILES, InChI, SMARTS, 2D/3D Structures, RDKit, and Open Babel
4. Day 09: Molecular Descriptors – Molecular Weight, LogP, TPSA, Hydrogen Bond Donors/Acceptors, and Rotatable Bonds
5. Day 10: Drug-Likeness Rules – Lipinski's Rule of Five, Veber, Ghose, Egan, QED Score, and Beyond Rule of Five

Week 3: Molecular Similarity + Chemical Databases

1. Day 11: Chemical Similarity Analysis – Tanimoto Coefficient, Morgan/ECFP Fingerprints, MACCS, and Scaffold Hopping
2. Day 12: Drug Discovery Databases – ChEMBL, PubChem, BindingDB, and ZINC20; Bioactivity Data Retrieval
3. Day 13: RDKit Practical – Installation, Generation of 2048-Bit Morgan Fingerprints, and Tanimoto Similarity for FDA-Approved Drugs
4. Day 14: Mini Project – Develop a Rule of Five Filter in Python Using SMILES Input and CSV Output
5. Day 15: Hit Discovery Strategies – High-Throughput Screening, Virtual Screening, Fragment-Based Screening, and Phenotypic Screening

Week 4: Molecular Docking + QSAR + Machine Learning

1. Day 16: Molecular Docking Fundamentals – Lock-and-Key Model, Scoring Functions, AutoDock Vina, and Docking Limitations
2. Day 17: Introduction to QSAR Modeling – Molecular Descriptors, Machine Learning, and IC50 Prediction
3. Day 18: QSAR Dataset Preparation – ChEMBL IC50 Data, Salt Removal, Duplicate Handling, and Activity Cliff Analysis
4. Day 19: Machine Learning for QSAR – Random Forest, SVM, XGBoost, ECFP4, R², RMSE, MAE, and Applicability Domain
5. Day 20: ADMET Prediction – SwissADME, admetSAR, pkCSM, CYP Inhibition, hERG, and Blood–Brain Barrier Prediction

Week 5: Lead Optimization + Structure-Based Drug Design

1. Day 21: Hands-on: Train a QSAR Model Using the Delaney ESOL Dataset and Predict Solubility
2. Day 22: Lead Optimization Strategies – Improving Potency, Selectivity, ADMET Properties, and Intellectual Property
3. Day 23: Bioisosteres and Scaffold Hopping – Chemical Group Replacement to Improve Toxicity and Pharmacokinetic Properties
4. Day 24: Structure-Based Drug Design (SBDD) – PDB Protein Structures, Binding Pocket Analysis, and PyMOL Visualization
5. Day 25: Ligand-Based Drug Design (LBDD) – Pharmacophore Modeling, 3D-QSAR, and Applications Without Crystal Structures

Week 6: Advanced Drug Design + Case Studies + Capstone

1. Day 26: Prodrugs and Drug Delivery – Ester/Amide Prodrugs, Nanocarriers, and Liposomes
2. Day 27: Case Study 1: Kinase Inhibitors – Imatinib Discovery, ATP-Binding Sites, and Resistance Mutations
3. Day 28: Case Study 2: GPCR-Targeted Drugs – Beta-Blockers, Antihistamines, Receptor Signaling, and Drug Design
4. Day 29: Capstone Project – Virtual Screening Workflow: PDB Target Selection, ZINC Compound Retrieval, Rule of Five Filtering, and Docking-Based Ranking
5. Day 30: Final Report and Presentation – Target Selection, Hit Identification, ADMET Filtering, and Top Five Virtual Hits; Future Directions in GNNs, Generative Models, and Tox21