Drug-Discovery

Computational Drug Discovery Part 2: Molecular Representations for Machine Learning

Molecules are converted into data for ML using SMILES, fingerprints, molecular graphs, and 3D geometry. The trade-off is between compactness (SMILES/fingerprints) and richness (graphs/3D geometry). The representation choice fundamentally determines what models can learn about binding and performance in drug discovery.

Computational Drug Discovery Part 1: Molecules, Proteins, and the Drug Discovery Challenge

An introduction to the fundamental biology behind drug discovery. We explore atoms, bonds, functional groups, and proteins, then examine the computational drug discovery pipeline from target identification to clinical trials. Learn why finding effective drugs is extraordinarily difficult and how AI is transforming this challenge.