AlphaFold 3-Based Drug Discovery: Docking, ADMET and Lead Prioritisation

To equip participants with the practical skills required to perform an end-to-end computational drug-discovery workflow, beginning with AlphaFold 3 protein structure assessment and ending with molecular docking, ADMET evaluation, toxicity screening, and scientific lead prioritisation.

• Understand the role of AI-powered protein structure prediction in modern drug discovery.
• Retrieve, generate, and critically assess protein structures using AlphaFold resources and experimental databases.
• Interpret structure-confidence parameters such as pLDDT, PAE, and ipTM.
• Prepare protein targets and candidate ligands for molecular docking.
• Perform molecular docking and evaluate binding affinity, pose quality, and important molecular interactions.
• Screen compounds for physicochemical properties, drug-likeness, lead-likeness, and medicinal chemistry alerts.
• Predict absorption, distribution, metabolism, excretion, transporter interactions, and clearance.
• Evaluate toxicity endpoints including hepatotoxicity, mutagenicity, carcinogenicity, acute toxicity, and hERG inhibition.
• Integrate docking, drug-likeness, ADME, and toxicity results into a weighted prioritisation matrix.
• Scientifically justify the selection of a final lead compound and suitable backup candidates.

📅 Day 1: AlphaFold 3 Structure Prediction and Target Preparation

Focus: Retrieving, evaluating and preparing reliable protein structures for molecular docking.

• Introduction to AI-powered protein structure prediction and structure-based drug discovery.
• Working principles and applications of AlphaFold 3.
• Overview of AlphaFold Database, AlphaFold Server, Protein Data Bank and UniProt.
• Retrieval of predicted and experimentally determined protein structures.
• Interpretation of pLDDT, PAE, ipTM and domain-confidence scores.
• Comparison of AlphaFold-predicted structures with experimental structures.
• Identification of active sites, binding pockets and functional residues.
• Protein cleaning, hydrogen addition, protonation and structural preparation.
• Limitations of predicted structures, flexible loops, missing residues and uncertain regions.

🛠️ Hands-on Activities

• Retrieve a disease-associated protein structure from AlphaFold Database or Protein Data Bank.
• Analyse confidence metrics and identify reliable and uncertain structural regions.
• Visualise the protein structure and identify potential ligand-binding pockets.
• Clean and prepare the selected protein structure for molecular docking.

🧰 Tools Covered: AlphaFold Database, AlphaFold Server, UniProt, Protein Data Bank, PyMOL or UCSF Chimera.

📅 Day 2: Molecular Docking and Protein–Ligand Interaction Analysis

Focus: Performing molecular docking and interpreting binding poses beyond docking scores.

• Principles of molecular docking and structure-based virtual screening.
• Retrieval of candidate compounds using PubChem, SMILES and SDF files.
• Ligand cleaning, protonation and geometry optimisation.
• Preparation of protein and ligand docking files.
• Selection of docking grids, search spaces and binding-site coordinates.
• Configuration of exhaustiveness and pose-generation parameters.
• Interpretation of docking scores, binding energies, RMSD and pose clustering.
• Analysis of hydrogen bonds, hydrophobic interactions, salt bridges and π-interactions.
• Limitations of docking scores and the importance of biological interpretation.

🛠️ Hands-on Activities

• Retrieve and prepare five to ten candidate compounds.
• Define the protein-binding site and docking search space.
• Perform molecular docking against the prepared protein target.
• Visualise and compare the top-ranked binding poses.
• Develop an initial interaction-based compound ranking.

🧰 Tools Covered: PubChem, AutoDock Vina through Google Colab, SwissDock or DockThor, PyMOL, Protein-Ligand Interaction Profiler.

📅 Day 3: ADMET Profiling, Toxicity Prediction and Lead Prioritisation

Focus: Integrating docking, drug-likeness, pharmacokinetic and toxicity results for rational lead selection.

• Drug-likeness and lead-likeness assessment using established medicinal chemistry filters.
• Evaluation of molecular weight, LogP, TPSA, solubility and molecular flexibility.
• Prediction of gastrointestinal absorption and oral bioavailability.
• Assessment of blood–brain barrier penetration and compound distribution.
• Analysis of CYP450 metabolism and drug–drug interaction risks.
• Evaluation of P-glycoprotein interactions, transporters and clearance.
• Prediction of hepatotoxicity, mutagenicity, carcinogenicity and acute toxicity.
• Assessment of cardiotoxicity and hERG-channel inhibition.
• Development of a weighted docking–ADMET lead-prioritisation matrix.

🛠️ Hands-on Activities

• Evaluate drug-likeness, lead-likeness and medicinal chemistry alerts.
• Predict absorption, distribution, metabolism and excretion properties.
• Assess toxicity classes, organ toxicity, mutagenicity and cardiotoxicity.
• Compare predictions obtained from multiple ADMET platforms.
• Build an integrated ranking matrix and select the final lead and backup compounds.

🧰 Tools Covered: SwissADME, pkCSM, ADMETlab 3.0, ProTox-II, PubChem, Microsoft Excel or Google Sheets

• Undergraduate and postgraduate students in Pharmacy, Biotechnology, Bioinformatics, Biochemistry, Molecular Biology, Computational Biology, Medicinal Chemistry, Pharmaceutical Chemistry, Pharmacology, and related disciplines
• PhD scholars and postdoctoral researchers working in computational drug discovery, structural bioinformatics, medicinal chemistry, cheminformatics, toxicology, and pharmaceutical research
• Faculty members and academicians teaching or supervising research in life sciences, biotechnology, pharmacy, bioinformatics, and healthcare sciences
• Research scientists involved in structure-based drug design, virtual screening, natural-product research, or drug repurposing
• Pharmaceutical and biotechnology professionals working in discovery research, lead optimisation, preclinical research, and computational modelling
• Professionals from contract research organisations involved in molecular modelling, screening, and early-stage drug development
• Researchers interested in AI-assisted drug discovery, precision medicine, natural compounds, and therapeutic-target evaluation