) values), commonly used as proxies for protein-ligand binding free energies. Because experimental reference data are often unavailable or collected using inconsistent techniques and/or procedures between laboratories, we developed two computational workflows that generate configurational ensembles of soluble protein-ligand complexes with Molecular Dynamics (MD) and compute the Absolute Binding Free Energies (ABFEs) of the sampled ligand binding poses with implicit-solvent calculations. The resulting consistent large-scale datasets of ABFEs address two complementary aspects of virtual screening: quantitative binding affinity estimation and binding pose assessment. Our Binding Affinity Prediction (BAP) workflow estimated protein-ligand binding affinities for 4000+ complexes from the PDBbind 2020 dataset. Our Pose Selector (PS) workflow computed non-convergence ABFEs from short Molecular Dynamics (MD) simulations, estimating the stability of 800,000+ related binding poses. To produce ABFE data at this scale, our free-energy workflows classify, check, and repair input structures of protein-ligand complexes in a fully automated fashion. The workflow scripts, molecular dynamics data, and ABFE labels are publicly available, creating an extendable database of reference values for the development of Scoring Functions for Large-Scale Virtual Screening campaigns.
Wingbermühle et al. (Wed,) studied this question.