The quality and consistency of training data remain critical bottlenecks for protein-ligand binding prediction. Public affinity datasets, aggregated from thousands of labs and assay formats, introduce biases that limit model generalization and complicate evaluation. DNA-encoded chemical libraries (DELs) offer a potential solution: unified experimental protocols generating massive binding datasets across diverse chemical and protein target space. We present Hermes, a lightweight transformer trained exclusively on DEL data from screens against hundreds of protein targets, representing one of the largest and most protein-diverse DEL training sets applied to protein-ligand interaction (PLI) modeling to date. Despite never seeing traditional affinity measurements during training, Hermes generalizes to held-out targets, novel chemical scaffolds, and external benchmarks derived from public binding data and high-throughput screens. Our results demonstrate that DEL data alone captures transferable protein-ligand interaction representations, while Hermes' minimal architecture enables inference speeds suitable for large-scale virtual screening.
@article{arxiv.2602.13503,
title = {Hermes: Large DEL Datasets Train Generalizable Protein-Ligand Binding Prediction Models},
author = {Maxwell Kleinsasser and Brayden J. Halverson and Edward Kraft and Sean Francis-Lyon and Sarah E. Hugo and Mackenzie R. Roman and Ben Miller and Andrew D. Blevins and Ian K. Quigley},
journal= {arXiv preprint arXiv:2602.13503},
year = {2026}
}