Precise Binding Free Energy Calculations for Multiple Molecules Using an Optimal Measurement Network of Pairwise Differences

Alchemical binding free energy (BFE)
calculations offer an efficient
and thermodynamically rigorous approach to in silico binding affinity
predictions. As a result of decades of methodological improvements
and recent advances in computer technology, alchemical BFE calculations
are now widely used in drug discovery research. They help guide the
prioritization of candidate drug molecules by predicting their binding
affinities for a biomolecular target of interest (and potentially
selectivity against undesirable antitargets). Statistical variance
associated with such calculations, however, may undermine the reliability
of their predictions, introducing uncertainty both in ranking candidate
molecules and in benchmarking their predictive accuracy. Here, we
present a computational method that substantially improves the statistical
precision in BFE calculations for a set of ligands binding to a common
receptor by dynamically allocating computational resources to different
BFE calculations according to an optimality objective established
in a previous work from our group and extended in this work. Our method,
termed Network Binding Free Energy (NetBFE), performs adaptive BFE
calculations in iterations, re-optimizing the allocations in each
iteration based on the statistical variances estimated from previous
iterations. Using examples of NetBFE calculations for protein binding
of congeneric ligand series, we demonstrate that NetBFE approaches
the optimal allocation in a small number (≤5) of iterations
and that NetBFE reduces the statistical variance in the BFE estimates
by approximately a factor of 2 when compared to a previously published
and widely used allocation method at the same total computational
cost.