AlphaFold 3-Based Drug Discovery: Docking, ADMET and Lead Prioritisation
Move from an AI-predicted protein structure to a scientifically prioritised lead using molecular docking, pharmacokinetic, and toxicity evidence.
About This Course
This three-day hands-on workshop introduces participants to an integrated computational drug-discovery workflow combining AlphaFold 3 protein structure prediction, molecular docking, drug-likeness analysis, ADMET profiling, toxicity prediction, and lead-compound prioritisation.
Participants will learn to evaluate protein structures, prepare targets and ligands, perform molecular docking, analyse protein–ligand interactions, assess pharmacokinetic and safety properties, and develop an evidence-based ranking matrix for final lead selection.
The workshop is suitable for students, researchers, academicians, pharmaceutical professionals, biotechnology professionals, and industry participants seeking practical skills in AI-assisted and structure-based drug discovery.
Aim
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.
Workshop Objectives
- 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.
Workshop Structure
📅 Day 1: AlphaFold 3 Structure Prediction and Target Preparation
Focus: Retrieving, evaluating and preparing reliable protein structures for molecular docking.
Topics Covered
- 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.
Topics Covered
- 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.
Topics Covered
- 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
Who Should Enrol?
- 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
Important Dates
Registration Ends
September 7, 2026
IST 4.30 pm
Workshop Dates
September 7, 2026 – September 9, 2026
IST 05:00 PM
Workshop Outcomes
- Select and evaluate an appropriate protein structure for molecular docking.
- Interpret AlphaFold confidence metrics and recognise unreliable structural regions.
- Prepare proteins and ligands for docking studies.
- Perform molecular docking and critically analyse protein–ligand interactions.
- Differentiate between favourable docking scores and biologically meaningful binding modes.
- Apply internationally recognised drug-likeness and medicinal chemistry criteria.
- Predict and compare major pharmacokinetic and transporter properties.
- Identify toxicity risks and major safety liabilities.
- Perform consensus analysis across multiple computational platforms.
- Construct a transparent and scientifically defensible lead-prioritisation matrix.
Meet Your Mentor(s)
Recording fee
Participants in India
₹2,499
Participants outside India
$50
- All session recordings
- Study material
- Certificate after completion
- 1-year access
The live sessions of this workshop are over, so the live fees no longer apply. The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.
Frequently asked questions
Will I get the recording?
Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).
What if I miss a live session?
Watch the recording in your dashboard and send your questions to the mentor through the support desk.
Do I get a certificate?
Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.
Which fee category applies to me?
Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.
How can I pay?
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
What is the refund policy?
See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy