  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 anti-targets).
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 binding free energy 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 binding free
energy estimates by approximately a factor of two when compared to a previously
published and widely used allocation method at the same total computational
cost.
