Binding Free Energies without Alchemy

TL;DR

The paper introduces Direct Binding Free Energy (DBFE), a novel end-state method that predicts binding affinities without alchemical intermediates, reducing computational cost and enabling efficient virtual screening.

Contribution

It presents DBFE, an end-state ABFE approach that eliminates the need for alchemical intermediates, simplifying calculations and improving efficiency in binding affinity predictions.

Findings

DBFE outperforms OBC2 double decoupling on a host-guest benchmark.

DBFE performs comparably to OBC2 MM/GBSA on a protein-ligand benchmark.

Requires only one complex simulation per ligand, reducing computational effort.

Abstract

Absolute Binding Free Energy (ABFE) methods are among the most accurate computational techniques for predicting protein-ligand binding affinities, but their utility is limited by the need for many simulations of alchemically modified intermediate states. We propose Direct Binding Free Energy (DBFE), an end-state ABFE method in implicit solvent that requires no alchemical intermediates. DBFE outperforms OBC2 double decoupling on a host-guest benchmark and performs comparably to OBC2 MM/GBSA on a protein-ligand benchmark. Since receptor and ligand simulations can be precomputed and amortized across compounds, DBFE requires only one complex simulation per ligand compared to the many lambda windows needed for double decoupling, making it a promising candidate for virtual screening workflows. We publicly release the code for this method at https://github.com/molecularmodelinglab/dbfe.