Performance and Analysis of the Alchemical Transfer Method for Binding Free Energy Predictions of Diverse Ligands

TL;DR

This paper validates the Alchemical Transfer Method (ATM) for predicting binding free energies across diverse protein-ligand complexes, demonstrating its simplicity, broad applicability, and comparable accuracy to state-of-the-art methods.

Contribution

The paper introduces a streamlined ATM approach with a novel coordinate perturbation scheme and dual-topology, enabling effective RBFE predictions without complex atom mapping or charge corrections.

Findings

ATM achieves accuracy comparable to existing methods.

ATM is simpler and more broadly applicable, especially for scaffold-hopping.

Over 500 calculations confirm ATM's robustness across diverse transformations.

Abstract

The Alchemical Transfer Method (ATM) is herein validated against the relative binding free energies of a diverse set of protein-ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and the AToM-OpenMM software to compute the relative binding free energies (RBFE) of the benchmark set prepared by Schindler and collaborators at Merck KGaA. This benchmark set includes examples of standard small R-group ligand modifications as well as more challenging scenarios, such as large R-group changes, scaffold hopping, formal charge changes, and charge-shifting transformations. The novel coordinate perturbation scheme and a dual-topology approach of ATM address some of the challenges of single-topology alchemical relative binding free energy methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and post-corrections for charge-changing perturbations. Thus, ATM is simpler and more broadly applicable than conventional alchemical methods, especially for scaffold-hopping and charge-changing transformations. Here, we performed well over 500 relative binding free energy calculations for eight protein targets and found that ATM achieves accuracy comparable to existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM is applicable as a production tool for relative binding free energy (RBFE) predictions across a wide range of perturbation types within a unified, open-source framework.