Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field

Designing
tight-binding ligands is a primary objective of small-molecule
drug discovery. Over the past few decades, free-energy calculations
have benefited from improved force fields and sampling algorithms,
as well as the advent of low-cost parallel computing. However, it
has proven to be challenging to reliably achieve the level of accuracy
that would be needed to guide lead optimization (∼5× in
binding affinity) for a wide range of ligands and protein targets.
Not surprisingly, widespread commercial application of free-energy
simulations has been limited due to the lack of large-scale validation
coupled with the technical challenges traditionally associated with
running these types of calculations. Here, we report an approach that
achieves an unprecedented level of accuracy across a broad range of
target classes and ligands, with retrospective results encompassing
200 ligands and a wide variety of chemical perturbations, many of
which involve significant changes in ligand chemical structures. In
addition, we have applied the method in prospective drug discovery
projects and found a significant improvement in the quality of the
compounds synthesized that have been predicted to be potent. Compounds
predicted to be potent by this approach have a substantial reduction
in false positives relative to compounds synthesized on the basis
of other computational or medicinal chemistry approaches. Furthermore,
the results are consistent with those obtained from our retrospective
studies, demonstrating the robustness and broad range of applicability
of this approach, which can be used to drive decisions in lead optimization.