Computational Drug Discovery Fundamentals Overview

Understand how computational drug discovery works and its major building blocks in a short series of blogs.

Miniseries / Collections

Computational Drug Discovery Part 3: Alphafold2 - Protein Structure Prediction (Miniseries)

An overview of the AlphaFold2 plus detailed elaboration of the building blocks and ideas introduced by AlphaFold2.

Computational Drug Discovery Part 4: Generative Models for De Novo Drug Design (Miniseries)

An overview of the generative models for de novo drug design.

Standalone Posts

Computational Drug Discovery Part 4: Graph Neural Networks for Molecular Property Prediction

A technical deep-dive into Graph Neural Networks (GNNs) for predicting molecular properties. Learn how to construct molecular graphs, implement message passing architectures, and apply attention mechanisms to drug discovery tasks.

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.