Key takeaways from the Optibrium panel discussion featuring Nathan Brown, Charlotte Deane, Pat Walters, Chris Swain, and Paul Czodrowski on AI strategy, data leakage, generative risks, and LLMs in drug discovery.
Jul 30, 2026
Limitations of scale-based evaluation in structural biology, and the transition toward physical grounding, out-of-distribution generalization, and biophysical developability validation.
Jul 21, 2026
Introduction to graph representation learning, neighborhood aggregation, and Deep Graph Library (DGL) workflows, including a glossary and tutorial reference map.
Jul 10, 2026
Limitations of static single-structure docking for flexible targets, and an integrated workflow combining molecular dynamics, clustering, and machine learning.
Jul 5, 2026
A pipeline I never wrote a line of worked flawlessly on the first try, and it left me with impostor syndrome. Here is why relying on AI agents is not cheating, but a permanent shift in what scientific expertise actually means.
Jun 23, 2026
Pinning random_state=42 makes a UMAP-based train/test split perfectly reproducible — and that is exactly why it can lull you into a false sense of rigor. Reproducibility guarantees you get the same answer every run; it says nothing about whether that answer is typical. Here's the distinction, why it matters for evaluating GNNs on chemical-domain shifts, and how to fix it.
Jun 23, 2026
I tried to reproduce the well-known PDBbind data-leakage effect with a small 3D GNN on a laptop — and couldn't. Two independent diagnostics show why: leakage only inflates models strong enough to memorize.
Jun 19, 2026
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
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
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