Everything you need to know before enrolling.
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Drug discovery pipeline, chemical data, SMILES, SDF, descriptors, RDKit, molecular properties, and Lipinski’s Rule of Five · 60 mins — practical lessons, demonstrations and guided work included in this module.
Chemical space, molecular fingerprints, similarity searching, 3D conformers, PyMOL/Chimera, and PDB visualization · 60 mins — practical lessons, demonstrations and guided work included in this module.
Protein–ligand docking, target and ligand preparation, AutoDock Vina, docking scores, binding poses, and KNIME workflows · 60 mins — practical lessons, demonstrations and guided work included in this module.
QSAR concepts, molecular descriptors, scikit-learn, Random Forest regression, ADMET prediction, and ADMETLab · 60 mins — practical lessons, demonstrations and guided work included in this module.
Generative AI for molecules, SMILES-based models, drug-likeness filtering, complete workflow integration, and project presentation · 60 mins — practical lessons, demonstrations and guided work included in this module.
Learn from experienced educators and practitioners through practical, career-focused bioinformatics training.
“Excellent course! The content is practical and the projects made the concepts much easier to apply.”
Ananya VermaPhD Scholar, India★★★★★“Hands-on projects made all the difference. The guided workflow made the analysis easier to follow.”
Rahul MehtaResearch Associate, Germany★★★★★“Very detailed and practical content. I feel much more confident now.”
Sneha PatelBioinformatics Analyst, India★★★★★Students, researchers, PhD scholars, biotechnology, pharmacy, chemistry, bioinformatics, computational biology, and life science learners can join.
No specific prerequisite knowledge is required according to the course page. Bio Help Learning
The bootcamp is conducted over 5 days.
Sessions are scheduled from 7:00–8:00 PM IST. Bio Help Learning
The bootcamp is conducted through live sessions.
Yes. RDKit is used for chemical data handling, molecular properties, fingerprints, conformer generation, and drug-likeness analysis.
Yes. The bootcamp 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 bootcamp 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 day 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. The course page states that a certificate is provided. Bio Help Learning
The joining link is sent to the registered email on the day the sessions begin. Bio Help Learning
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