Learn how cheminformatics and AI speed up drug discovery: RDKit, virtual screening, AutoDock Vina docking, QSAR models and generative molecule design.
Everything you need to know before enrolling.
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Learn at your own pace with recordings
Modern drug discovery relies on computers long before a compound reaches the lab. In this course you learn the cheminformatics and AI workflow that research teams use to find, test and improve drug candidates on screen.
You start by representing molecules as SMILES and calculating their properties with RDKit, then screen chemical libraries, dock ligands into protein targets with AutoDock Vina, build QSAR and ADMET prediction models with scikit-learn, and finish by generating and filtering new molecules with AI.
Learn from experienced educators and practitioners through practical, career-focused bioinformatics training.
18 answers about this course
Students, researchers, PhD scholars, biotechnology, pharmacy, chemistry, bioinformatics, computational biology, and life science learners can join.
No specific prerequisite knowledge is required.
The course is organised into 5 recorded modules that you can complete at your own pace.
Anytime. All sessions are recorded, so you can learn on your own schedule.
The course is delivered through recorded sessions.
Yes. RDKit is used for chemical data handling, molecular properties, fingerprints, conformer generation, and drug-likeness analysis.
Yes. The course includes protein–ligand preparation, AutoDock Vina docking, docking scores, binding poses, and result analysis.
AutoDock Vina is used for docking, with PyMOL or UCSF Chimera for visualization and analysis.
Yes. The course covers chemical-space exploration, molecular fingerprints, similarity searching, and virtual screening concepts.
Yes. The course introduces QSAR modeling and machine learning using scikit-learn.
A Random Forest Regressor is used for molecular property prediction.
Yes. ADMET concepts and practical prediction using ADMETLab are included.
Yes. The final session introduces AI-driven molecular generation using pre-trained generative models.
Yes. Each module includes project tasks that build toward a complete mini drug discovery project.
The project integrates compound selection, screening, docking, QSAR/ML, ADMET prediction, and selection of potential lead compounds.
Yes. A certificate of completion is provided.
Access details are shared after registration. If you need help, message us on WhatsApp at +91 78280 25951.
You get full access to all course materials for 3 months from the date of enrollment.
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