Courses
Practical, code-first courses on AI and computational drug discovery — for scientists who want to build real things.
Every concept illustrated with working Python code. No theory without practice.
Drawn from real workflows in drug discovery — not textbook examples.
Work through material at your own speed. All content available immediately on enrolment.
Introduction to Computational Drug Discovery
A practical, from-scratch introduction to the computational tools and concepts used in modern drug discovery — from target biology to hit identification.
Generative AI for Molecular Design
Deep-dive into diffusion models, graph neural networks, and flow-matching approaches for de novo molecular generation. Hands-on PyTorch throughout.
ADMET Prediction with Machine Learning
Build and evaluate ML models for predicting absorption, distribution, metabolism, excretion, and toxicity properties of drug candidates.
In-Depth Molecular Docking
A comprehensive, hands-on course on structure-based virtual screening — receptor and ligand preparation, grid/search-space setup, docking with AutoDock Vina, and rigorous results analysis.
In-Depth Molecular Dynamics Simulations with GROMACS
From system setup to production runs and trajectory analysis — a complete, practical guide to running protein-ligand molecular dynamics simulations in GROMACS.