Cheminformatics & Drug Discovery

This program provides a structured introduction to cheminformatics concepts and computational approaches used in modern drug discovery. The syllabus covers chemical databases such as public and proprietary repositories, data retrieval methods, and compound library management.

Students learn molecular descriptors, fingerprints, and similarity searching, along with Quantitative Structure–Activity Relationship (QSAR) modeling principles, validation techniques, and interpretation of results. The course also introduces key cheminformatics tools and software used for virtual screening, lead identification, and optimization.

Additional modules include chemical space analysis, ADMET prediction basics, data curation, structure representation formats (SMILES, InChI), and integration of cheminformatics with bioinformatics and AI-based drug discovery workflows.

Practical learning is reinforced through hands-on assignments, case studies, and assessments, enabling students to understand real-world applications in pharmaceutical and biotechnology research.

Eligibility: Life science students.
Outcome: Skill-based certification suitable for roles in drug discovery, computational chemistry, and pharmaceutical R&D.