School of Scientific Computing & Informatics · Chemo- & materials informatics
Python Programming for Cheminformatics
Cheminformatics is chemistry written as code. This course teaches you to read, transform, search and analyse molecules programmatically with Python and RDKit, the open-source toolkit used across pharmaceutical research.
Course overview
Python Programming for Cheminformatics treats molecules as data structures: parsing SMILES and SDF files, sanitising and standardising structures, computing descriptors and fingerprints, and searching by substructure and similarity.
You then build real pipelines: retrieving compounds from PubChem and ChEMBL through their APIs, enumerating reactions and analogues, clustering libraries, and packaging the code so colleagues can reuse it.
Learning outcomes
By the end of the course you will be able to:
- Parse, standardise and write molecules in common formats
- Compute descriptors and fingerprints and compare molecules
- Run substructure, similarity and SMARTS searches
- Retrieve and curate data from PubChem and ChEMBL programmatically
- Build, test and share a cheminformatics pipeline
Skills you will practise
Eligibility and prerequisites
Who can apply
- Students and graduates in any science, engineering, pharmacy, medicine, agriculture, geography or computing programme (B.Sc., B.Tech., B.E., BCA, M.Sc., M.Tech., MCA, PhD)
- Researchers, faculty and working professionals in research, healthcare, biotech, pharma or IT who want to apply computing to science
- Final-year students may apply before their results are declared
Prerequisites
- Python basics
- Undergraduate organic chemistry
Best suited to
- Chemistry and pharmacy students who want to code
- Medicinal chemists automating routine work
- Data scientists entering drug discovery
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- SMILES, InChI and molfiles
- Molecular graphs in RDKit
- Sanitisation and standardisation
Topics
- Physicochemical descriptors
- Morgan and MACCS fingerprints
- Similarity metrics
Topics
- Substructure and SMARTS
- Maximum common substructure
- Scaffolds
Topics
- PubChem PUG-REST
- ChEMBL web services
- Curation and deduplication
Topics
- Reaction SMARTS
- Enumerating analogues
- Clustering and diversity
Topics
- Design
- Tests
- Documentation
Practical sessions and projects
Capstone project
Ship a cheminformatics tool
Write, test and document a Python tool that curates, searches or analyses compound data for a real research question.
Assessed on the correctness of the analysis, the quality of the code and the reasoning in the write-up. A pass is required for the certificate.
- Molecules as code
Parse and standardise a messy compound list and draw the molecules.
- Descriptors and fingerprints
Compute descriptors for an approved-drug set and find nearest neighbours by Tanimoto similarity.
- Searching chemistry
Write SMARTS patterns for functional groups and search a library for them.
- Chemical data from the web
Download and curate bioactivity data for a target into an analysis-ready table.
- Reactions and libraries
Enumerate an amide library from building blocks and select a diverse subset.
- Project: a reusable chemistry tool
Build and document a command-line or notebook tool that solves a real cheminformatics task.
Software, tools and datasets
Software and tools
All software is free and open source or free for academic use. Recommended computer: 8 GB RAM, 30 GB free disk, Linux, macOS or Windows with WSL2.
Datasets
- ChEMBL bioactivity data: Data retrieval and curation.ChEMBL (CC BY-SA 3.0)
- PubChem compound records: Programmatic access.PubChem (public domain)
Core reading
- Landrum G. RDKit: Open-source cheminformatics (rdkit.org) and the RDKit Book.
- Kim S et al. (2023). PubChem 2023 update. Nucleic Acids Research 51:D1373–D1380.
Duration, format and fees
- Next cohort
- Dates are sent with your admission offer
- Timetable
- Three evening or weekend sessions a week (IST), plus a lab clinic; intensive and weekend-only schedules on request
- Payment
- Secure online payment link with your admission offer (UPI, cards, net banking). Instalments on request for fees above ₹15,000.
- Refunds
- See the refund and cancellation policy.
Instructor
The instructor for each cohort is named in your admission offer and on this page when the cohort is announced. Vedicgen instructors are selected for hands-on research or industry experience in the subject they teach, and every course is reviewed by a second subject expert.
Questions about who teaches a cohort? [email protected]
Certificate
Learners who complete the course and pass its capstone receive a Vedicgen certificate of completion for Python Programming for Cheminformatics, issued by Vivofold, stating the guided hours and skills assessed. Each certificate has a unique ID and QR code that anyone can check.
Apply for Python Programming for Cheminformatics
Apply
Send the form below. It takes about five minutes.
Review
An adviser confirms your fit and format within two working days.
Offer
You receive dates, timetable and a secure payment link.
Onboard
Set-up guide, pre-reading and joining links.
Frequently asked questions
In the live cohort, modules are taught in instructor-led sessions with a weekly lab clinic where you work on real public data with help at hand; every session is recorded. The self-paced format uses the same lessons, labs and datasets with mentor review of your capstone. Both formats end with an assessed project reviewed for the code and the science.
A laptop with at least 8 GB of RAM and about 30 GB of free disk space, running Linux, macOS, or Windows with WSL2. Installation instructions are sent before the first session, and lab clinics help with any set-up problems.
Yes. Learners who complete the course and pass its assessment receive a Vedicgen certificate of completion, issued by Vivofold. Every certificate carries a unique ID and a QR code that anyone, including an employer or admissions office, can check on the certificate verification page.
Apply with the form on this page. Live and in-person cohorts run through the year; the dates, timetable and joining details of the next cohort are sent with your admission offer and listed on this page once announced. Self-paced enrolment opens as soon as your application is accepted.
Yes. No course runs longer than four weeks, and the length is customisable: choose the standard schedule, an intensive one- to two-week schedule, weekends only, or a custom timetable. Pick your preference on the application form; colleges and teams can book their own dates.
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