Machine-learning force-field scoring rivals free-energy perturbation for congeneric ligand ranking across public benchmarks

Relative binding free-energy (RBFE) methods such as free-energy perturbation (FEP) set the accuracy standard for ranking congeneric ligands in structurebased drug design, but their computational cost limits throughput. Machine-learned force fields (MLFFs) now approach quantum-chemical accuracy at a small fraction of that cost, raising the question of whether they can recover much of RBFE’s ranking accuracy while remaining computationally efficient. We introduce a static-score methodology called taqo and benchmark 10 MLFF variants spanning 6 model families (UMA, MACE, Orb, eSEN, AIMNet2, AllScAIP), together with a classical molecular-mechanics force field, on a uniform staticpocket protocol, correlated against experiment across 22 benchmark systems.We find that the ten MLFFs cannot be resolved from one another at this sample size, while every one of them significantly outranks the classical force field reference, at a few seconds per ligand on a single GPU. Against published OpenFE and FEP+ results on the same systems and the same ligand sets, taqo is statistically indistinguishable from OpenFE and significantly below FEP+, at roughly three orders of magnitude lower cost. On the congeneric JACS subset taqo and FEP+ are statistically indistinguishable. On the same benchmark poses taqo outperforms classical docking scores and is statistically indistinguishable from deep-learning affinity models, without any affinity-specific training. Together these establish MLFF interaction-energy scoring as a practical high-throughput approach for lead optimization.