Drug Discovery using AI Full Syllabus

Module 1: Introduction to AI in Drug Discovery

• Traditional vs AI-based drug discovery
• Overview of drug development lifecycle
• Where AI fits in (target identification → clinical trials)
• Key benefits: Time-saving, cost reduction, higher accuracy

Module 2: Biology & Chemistry Basics (For AI Learners)

• DNA, RNA, proteins: basics of molecular biology
• Receptors & enzymes in drug targeting
• Small molecules vs biologics
• Chemical properties & molecular structures

Module 3: Types of Data in Drug Discovery

• Genomic & proteomic data
• Molecular compound libraries (SMILES, InChI)
• Bioactivity data (IC50, EC50, etc.)
• Sources: PubChem, ChEMBL, DrugBank, BindingDB

Module 4: Data Preparation & Feature Engineering

• Molecular fingerprinting (MACCS, ECFP)
• One-hot encoding for sequences
• Descriptor calculation (molecular weight, logP)
• Handling imbalanced datasets (active vs inactive compounds)

Module 5: Machine Learning Models for Drug Discovery

• Classification models for compound activity prediction
• Regression models for drug-target binding affinity
• Random Forest, SVM, XGBoost in drug datasets
• Model interpretability in biomedical research

Module 6: Deep Learning for Molecule Generation & Prediction

• CNNs for 2D/3D molecular image analysis
• RNNs for SMILES sequence generation
• Autoencoders for molecule optimization
• Graph Neural Networks (GNNs) for molecular graphs

Module 7: AI in Target Identification & Validation

• Disease gene mapping with ML
• Predicting protein-drug interactions
• Docking score prediction using AI
• Tools: DeepChem, AlphaFold, BindingDB

Module 8: De Novo Drug Design

• Generative Models: GANs, VAEs for molecule generation
• Reinforcement Learning in compound optimization
• Synthetic accessibility & drug-likeness prediction
• Toxicity filtering using AI

Module 9: Virtual Screening Using AI

• Ligand-based vs structure-based screening
• ML-assisted scoring & ranking of molecules
• ADMET property prediction (Absorption, Distribution, Metabolism, Excretion, Toxicity)
• Tools: PyRx, Deep Docking, Mol2Vec

Module 10: Clinical Trials & Predictive Modeling

• Patient selection models using genomics
• Predicting success/failure in trials with ML
• Electronic Health Record (EHR) integration
• Case study: Drug repurposing using AI (e.g. COVID-19)

Module 11: AI Tools & Platforms in Drug Discovery

• Cheminformatics Libraries: RDKit, Open Babel
• AI Platforms: DeepChem, Chemprop, IBM RXN, Insilico Medicine
• Datasets: ZINC, Tox21, DrugBank

Module 12: Regulatory, Ethics & Future Trends

• FDA guidance for AI-based drug models
• Transparency & explainability in drug prediction
• AI bias in drug datasets
• Future: Quantum ML in drug discovery

Capstone Project (Recommended)

• Predict binding affinity of small molecules for a protein target
• Use DeepChem + ML/DL models
• Visualize predictions and propose top candidate molecules

Languages & Tools

• Python (RDKit, DeepChem, Scikit-learn, TensorFlow)
• Jupyter Notebook / Google Colab
• PyTorch or TensorFlow (for advanced users)
• Visualization: Seaborn, Matplotlib, Plotly