Absolute Binding Free Energy Calculations between the SARS-CoV-2 Main Protease and 130 Drug Leads Using Implicit Ligand Theory

Absolute binding free energy (ΔG) calculations can rank structurally diverse compounds, which could be useful for early-stage drug discovery. Unfortunately, for flexible systems, it can be challenging to sample the receptor conformations necessary to obtain converged ΔG calculations. Here, we address this challenge by leveraging extensive molecular dynamics simulations of apo SARS-CoV-2 main protease (MPro) that were conducted on the Folding@Home distributed computing system. A Markov state model (MSM) was built to compute the equilibrium probability of each snapshot. Representative snapshots were selected from clusters defined based on occupancy fingerprints of the catalytic site. The binding potential of mean force (BPMF), the binding free energy between a ligand and rigid receptor configuration, was computed between the representative snapshots and 130 drug leads from the COVID Moonshot, an open-source drug discovery project. ΔGs were computed using an exponential average of BPMFs based on implicit ligand theory (ILT). ΔG calculations recapitulated experimental values with a Pearson R of 0.55 and a mean-adjusted root-mean-square error of 1.6 kcal/mol. Accuracy and computational costs were found to be intermediate between docking and previous free energy calculations with a fully flexible receptor. Moreover, in 88% of systems, the calculated ΔG of the native binding pose (RMSD from crystallographic <3 Å) was within 1 kT of the top-ranked pose.