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

L Chen, Y Wu, C Wu, A Silveira… - Journal of Chemical …, 2023 - ACS Publications
Journal of Chemical Information and Modeling, 2023ACS Publications
The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free
energies (RBFEs) of a diverse set of protein–ligand complexes. We employed a streamlined
setup workflow, a bespoke force field, and AToM-OpenMM software to compute the RBFEs
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 …
The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free energies (RBFEs) of a diverse set of protein–ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and AToM-OpenMM software to compute the RBFEs 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 RBFE methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and postcorrections 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 RBFE calculations for eight protein targets and found that ATM achieves accuracy comparable to that of existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into the specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM can be applied as a production tool for RBFE predictions across a wide range of perturbation types within a unified, open-source framework.
ACS Publications
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