Molecular Dynamics Workflows to Compute Large-Scale Sets of Absolute Binding Free Energies Aiding Drug Candidate and...

Large-scale Virtual Screening (VS) campaigns of compound libraries can significantly speed up candidate selection in the early stages of drug discovery. The most promising drug candidates are identified by Scoring Functions (SFs), which enable VS campaigns to rank candidate compounds according to their estimated binding affinities. These SFs are typically trained on experimental data reflecting binding affinities (e.g., Dissociation Constant (K_d) 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.