A History of Graph Neural Networks in Drug Discovery
Graphs in drug discovery have gone from a quiet background tool to one of the main ways we think about molecules, proteins, and their interactions. This post walks through that story: how the field moved from fingerprints and QSAR to today’s 3D, attention-based graph neural networks operating directly on protein-ligand complexes.
Jun 8, 2026
Beyond SMILES: The Evolving Landscape of Molecular Representations
This post summarizes the key ideas from Zhang et al. (2026), “Molecular Knowledge Representations in the Era of Artificial Intelligence,” a preprint published on ChemRxiv (DOI: 10.26434/chemrxiv.15002830/v1). The Core Problem Molecules are quantum-mechanical objects. Their exact description is computationally intractable, and any real sample is a messy mixture of impurities, conformers, and side products. This means every representation of a molecule is, by necessity, an approximation — shaped by the interactions and length scales we care about.
May 23, 2026
Computational Strategies for Accelerating Drug Discovery: A Comprehensive Review
A comprehensive walkthrough of cheminformatics, machine learning, molecular docking, ADMET prediction, and molecular dynamics simulations as the modern toolbox for computer-aided drug discovery.
May 10, 2026
Beyond 2D Fingerprints: Encoding Protein-Ligand Interactions for Machine Learning
A practical guide to three advanced 3D fingerprinting methods (PLEC, SPLIF, and E3FP) and how to choose between them when featurizing docking poses for ML-based drug discovery models.
Apr 1, 2026
Awesome Drug Discovery
Aug 25, 2025
QSARBioPred
May 7, 2024
Computational Drug Repurposing with Multiscale Interactomes
A step-by-step workflow to identify repurposing candidates by integrating heterogeneous biomedical networks, graph embeddings, and experimental prioritization.
Apr 23, 2024
EnsembleBBB
Feb 19, 2024
CoumarinDB
Jan 25, 2024
Interpretable Machine Learning as a Key to Understanding BBB Permeability
Step-by-step tutorial on building an explainable ML model to predict blood-brain barrier permeability, using feature importance and SHAP analysis for interpretability.
Jan 23, 2024