SELF-PACED | ONLINE | GO FROM BEGINNER TO ADVANCED
🚨 NOTE 🚨: This course is offered only in self-paced mode. To access the course, enroll using the Enroll Now button and you will immediately get access to all course materials in your dashboard. There are no live cohort sessions. Please ignore any live start dates shown on the website. Support is available via Slack and email.
The most practical cheminformatics program for the AI-driven era
This course teaches you how to work with molecular data correctly. You will learn how chemical structures are represented, stored, searched, compared, and transformed in real discovery pipelines. The focus is applied cheminformatics for machine learning and generative modeling. You will build the data foundation.
You will build practical workflows for molecular similarity, substructure analysis, SAR exploration, virtual screening, and library design using industry-standard tools. The goal is operational competence. By the end, you should be able to take a raw chemical dataset and turn it into something usable, analyzable, and defensible.
This course forms the technical foundation required for any serious work in molecular modeling, QSAR, or generative chemistry. It is about understanding chemical data, not producing models that hide broken assumptions.
WHO THIS IS FOR
This course is for you if you work with chemical structures and datasets and want to do it properly. That includes medicinal chemists moving into data-driven workflows, computational chemists who want stronger fundamentals, cheminformatics engineers, and PhD students or postdocs working with molecular data.
This course is also the right primer if you plan to move into molecular AI or molecule generation later, because it gives you the chemical data foundations those models depend on.
WHAT YOU WILL BE ABLE TO DO
By the end of the course, you will be able to clean and standardize real-world chemical datasets, represent molecules using appropriate fingerprints and descriptors, and perform similarity, clustering, and SAR analyses with clear reasoning.
You will be able to design and evaluate virtual screening workflows, build and analyze combinatorial libraries, perform scaffold and substructure analysis, and make technically defensible decisions about chemical space exploration.
You will not train generative models or neural networks in this course. Instead, you will gain the core cheminformatics competence required to judge whether downstream modeling, including generative AI, is even being done correctly.
Capstone Projects and Certificate
You will complete 3 hands-on capstone projects that apply the full workflow end-to-end, from chemical data preparation to analysis and decision making. After successfully completing all capstones, you will receive a course completion certificate that you can request from your dashboard.
