Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series

Abstract

The accurate prediction of protein−ligand binding affinities is crucial for drug discovery. Alchemical free energy calculations have become a popular tool for this purpose. However, the accuracy and reliability of these methods can vary depending on the methodology. In this study, we evaluate the performance of a relative binding free energy protocol based on the alchemical transfer method (ATM), a novel approach based on a coordinate transformation that swaps the positions of two ligands. The results show that ATM matches the performance of more complex free energy perturbation (FEP) methods in terms of Pearson correlation but with marginally higher mean absolute errors. This study shows that the ATM method is competitive compared to more traditional methods in speed and accuracy and offers the advantage of being applicable with any potential energy function.

Modern Alchemical Free Energy Methods for Drug Discovery Explained

“…One such promising method is the alchemical transfer method (ATM), a novel approach based on a coordinate transformation that swaps the positions of two ligands. The method has been tested successfully on ligands with diverse scaffolds and offers the advantage of being applicable with any potential energy function. , …”

What to Make of Zero: Resolving the Statistical Noise from Conformational Reorganization in Alchemical Binding Free Energy Estimates with Metadynamics Sampling

“…The RBFE is then the reversible work along the alchemical path. 20−23 While increasingly popular, as evidenced by extensive largescale benchmarking validation studies against experimental data, 10,12,15,17,19 RBFE models do not always yield correct predictions. The causes of mispredictions are often unclear, primarily because the ground truth value of the models is not known and the relative contributions of model accuracy and statistical fluctuations on the prediction accuracy are uncertain.…”

“…The current fully open-source software release of ATM employs the OpenMM MD engine and has been successfully tested on a series of medicinal targets by us and academic and industrial partners. 19,43 In this work, we study the bias and variance of ATM by estimating the binding free energies of a series of complexes from the benchmark set of Schindler et al 12 relative to themselves (self-RBFEs). A self-RBFE is obtained when the two ligands considered in an ATM RBFE calculation are the same ligand.…”

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

“…Alchemical binding-free-energy prediction tools are emerging as a best-in-class standard for in silico prediction of binding-free energies (i.e., protein–ligand binding affinity) in a structure-based drug design. − While many challenges remain, , the increased utilization of binding free energy (RBFE) calculations has been fueled in part by the promising results of large-scale validation campaigns against benchmark sets representative of actual drug discovery projects. − …”

“…For the same reason, it applies to any molecular energy function, including the next generation of polarizable, − quantum-mechanical, − and machine-learning potentials , that are starting to be employed in macromolecular simulations. The current open-source software release of ATM employs the OpenMM MD engine and has been successfully tested on a series of drug discovery targets with the AMBER molecular mechanics force field in academic and industrial settings. , …”