Benchmark asks whether structure-based methods rescue DEL-trained ML out of distribution
- A recent NeurIPS competition showed top ML models trained on DEL data failing to generalize to out-of-distribution chemical space; this study tests whether structural modeling can bridge the gap.
- It benchmarks ML, Glide docking, GALigandDock and Boltz-2 cofolding on three biologically diverse targets screened against DELs built in several synthesis formats.
- ML does well in-distribution; the paper's focus is how each approach holds up on new chemistry.
💡 Points
DEL-trained models that shine on random splits and collapse on new chemotypes is exactly what bites when you try to seed a different series from a DEL campaign. Three targets is a small panel, but a like-for-like comparison with physics-based methods on identical data is useful. I'd use it to calibrate how much to trust ML triage on scaffolds the library never sampled.