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
A practical map of protein and protein-ligand descriptors for machine learning modeling, from sequence-only features to MD-derived structural and interaction features.
Jul 20, 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
Partial charges quietly drive the electrostatics behind every docking score. This guide compares the common semi-empirical options — AM1-BCC, PM6, AM1/PM3, Gasteiger and RESP — and gives a practical recommendation for when to use each.
Jun 23, 2026
A step-by-step, fully open-source pipeline that turns raw Protein Data Bank structures into a ligand-based 3D pharmacophore — mining the PDB, aligning binding pockets, clustering ligands, fixing bond orders, and distilling a consensus feature map ready for virtual screening.
Jun 23, 2026
AI in drug discovery is moving past one-shot predictions toward autonomous agents that plan experiments, write their own analysis code, call docking and QM tools, and critique their results. Here's what the shift means, the design patterns behind it, and five open-source agents worth examining — with an honest look at the caveats.
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