AI-Driven Drug Discovery: Target Identification, Virtual Screening, Molecular Docking & ADMET Prediction

• Understand the major stages of computational drug discovery.
• Identify and prioritise disease-associated therapeutic targets.
• Retrieve and shortlist biologically relevant candidate compounds.
• Perform molecular docking and interpret protein–ligand interactions.
• Generate molecular descriptors and fingerprints for predictive modelling.
• Build basic QSAR and machine-learning models for activity prediction.
• Evaluate drug-likeness and ADMET properties of shortlisted compounds.
• Integrate multiple computational results to prioritise potential lead candidates.

📅 Day 1: Target Discovery, Validation and Compound Shortlisting

Topics Covered

• Introduction to AI-assisted target discovery and early-stage drug discovery workflows.
• Disease-gene association and therapeutic target identification.
• Biological relevance, druggability and target-prioritisation criteria.
• Protein-function annotation and pathway-level interpretation.
• Protein-protein interaction network analysis.
• Exploration of chemical and bioactivity databases.
• Retrieval of known ligands, inhibitors and related bioactive compounds.
• Molecular descriptors and physicochemical property analysis.
• Drug-likeness and lead-likeness based compound filtering.
• Development of a focused compound library for virtual screening.

• Identify disease-associated genes using public biomedical databases.
• Prioritise a therapeutic protein target based on biological and network evidence.
• Analyse the selected target's protein interaction network.
• Retrieve relevant bioactive compounds from PubChem and ChEMBL.
• Generate molecular descriptors using RDKit.
• Apply basic physicochemical and drug-likeness filters.
• Build a focused compound shortlist for molecular docking.

🧰 Tools Covered:

📅 Day 2: Virtual Screening, Molecular Docking and Interaction Analysis

Topics Covered

• Principles of structure-based virtual screening and molecular docking.
• Retrieval and evaluation of experimentally determined protein structures.
• Protein cleaning, hydrogen addition and structural preparation.
• Ligand preparation, protonation and geometry optimisation.
• Identification of active sites and ligand-binding pockets.
• Selection of docking-grid coordinates and search-space parameters.
• Configuration of docking exhaustiveness and pose-generation settings.
• Interpretation of docking scores, binding affinities and pose orientations.
• Analysis of hydrogen bonds, hydrophobic interactions, salt bridges and aromatic interactions.
• Comparison of candidate binding modes and interaction profiles.
• Limitations of docking scores and importance of biological interpretation.

🛠️ Hands-on Activities

• Retrieve and prepare the selected protein structure.
• Prepare the shortlisted compounds for docking.
• Identify the binding pocket and define the docking search space.
• Perform molecular docking using AutoDock Vina.
• Compare binding scores and top-ranked poses.
• Visualise protein-ligand interactions.
• Generate an interaction-based ranking of the strongest candidates.

🧰 Tools Covered:

📅 Day 3: AI-Based Activity Prediction, ADMET Profiling and Lead Prioritisation

Topics Covered

• Molecular fingerprints and descriptor-based compound representation.
• Introduction to QSAR and machine-learning-based activity prediction.
• Preparation of molecular datasets for predictive modelling.
• Random Forest and XGBoost algorithms for compound activity prediction.
• Model training, validation and performance evaluation.
• Interpretation of molecular features using SHAP.
• Prediction of drug-likeness and pharmacokinetic properties.
• Assessment of absorption, distribution, metabolism and excretion.
• Toxicity prediction and medicinal chemistry alerts.
• Integration of docking, predicted activity and ADMET results.
• Multi-parameter lead scoring and compound prioritisation.
• Selection of primary and backup lead candidates.

🛠️ Hands-on Activities

• Generate molecular fingerprints and descriptors using RDKit.
• Build and evaluate a machine-learning activity-prediction model.
• Predict biological activity for shortlisted compounds.
• Interpret important molecular features using SHAP.
• Evaluate drug-likeness and ADMET properties using SwissADME.
• Compare docking, predicted activity and pharmacokinetic results.
• Develop an integrated lead-prioritisation matrix.
• Select the final lead and backup candidates.

• Identify and prioritise disease-associated therapeutic targets using bioinformatics databases.
• Retrieve, analyse, and shortlist bioactive compounds for computational screening.
• Perform molecular docking and interpret protein–ligand interactions and binding poses.
• Generate molecular descriptors and fingerprints using RDKit.
• Build basic QSAR and machine-learning models for compound activity prediction.
• Interpret predictive models using feature importance and SHAP.
• Evaluate drug-likeness, pharmacokinetic, and ADMET properties of candidate compounds.
• Integrate docking, AI predictions, and ADMET results for lead prioritisation.
• Develop an end-to-end AI-assisted computational drug-discovery workflow from target identification to lead selection.

• Undergraduate and postgraduate students in Biotechnology, Bioinformatics, Pharmacy, Life Sciences, Chemistry, and related disciplines.
• PhD Scholars and Researchers working in drug discovery, computational biology, pharmaceutical sciences, or molecular modelling.
• Academicians and Faculty interested in integrating AI and computational approaches into research and teaching.
• Biotechnology and Pharmaceutical Industry Professionals.
• Bioinformatics, Cheminformatics, and Computational Biology Professionals.
• Beginners transitioning into computational or AI-assisted drug discovery.