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

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 (Kd) values), commonly used as proxies for protein-ligand binding free energies. Since experimental reference data are often inconsistent between laboratories or even unavailable, we developed two computational workflows using Molecular Dynamics (MD) and implicit-solvent calculations to provide consistent large-scale datasets of Absolute Binding Free Energies (ABFEs), addressing 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-converged ABFEs from reduced-time Molecular Dynamics (MD) simulations estimating stability for 800,000+ related binding poses.