Machine Learning and Docking Methods Show Uneven Generalization on DNA-Encoded Library Screens
- The study benchmarks machine learning, physics-based docking, and cofolding methods for predicting protein-ligand binding using DNA-encoded library (DEL) screening data.
- Methods compared include state-of-the-art ML models alongside physics-based docking tools (Schrodinger Glide, Rosetta GALigandDock) and the cofolding method Boltz-2.
- Ground-truth binding data came from ultralarge DEL selection experiments across three biologically diverse protein targets and multiple DEL synthesis formats.
- The work follows a NeurIPS competition in which even top-performing ML models trained on DEL data failed to generalize to out-of-distribution (OOD) chemical space.
- ML models performed well in-distribution, but out-of-distribution hit discrimination depended on the specific target and ligand context, with no single method consistently dominating across cases.
- The authors conclude that benchmark performance alone cannot predict OOD generalization and release an open-source package, DEL-iver, for evaluating binding-prediction methods against DEL screening data.
This is the paper you send to anyone pushing to swap your docking rescore step for a single ML model. "No method dominates OOD" is the whole ballgame: your real triage work is always on a scaffold the model hasn't seen, and a benchmark AUC built on in-distribution DEL data tells you almost nothing about how a model will rank the novel matter you bring in next week. I don't read this as an argument against ML - Boltz-2 and friends are genuinely useful, fast filters - but as a case for running physics-based docking/cofolding in parallel, not as a fallback you only reach for when ML looks shaky. Target- and ligand-context dependence also means there's no one 'best' pipeline to standardize on company-wide; you validate per target, per chemotype, every time. DEL-iver going open-source is the practically useful bit - a shared OOD benchmark beats trusting a vendor's Tanimoto-flattered internal numbers.