Application of the Alchemical Transfer and Potential of Mean Force Methods to the SAMPL8 Host-Guest Blinded Challenge

TL;DR

This study compares two different free energy calculation methods, ATM and PMF, in predicting host-guest binding affinities within the SAMPL8 challenge, demonstrating their high accuracy and agreement with experimental data.

Contribution

The paper introduces and validates the ATM method alongside the established PMF approach, showing their consistent results and agreement with experimental binding affinities.

Findings

ATM and PMF methods agree closely in predictions

Both methods show high correlation with experimental data

Results validate the ATM protocol for future complex systems

Abstract

We report the results of our participation in the SAMPL8 GDCC Blind Challenge for host-guest binding affinity predictions. Absolute binding affinity prediction is of central importance to the biophysics of molecular association and pharmaceutical discovery. The blinded SAMPL series have provided an important forum for assessing the reliability of binding free energy methods in an objective way. In this blinded challenge, we employed two binding free energy methods, the newly developed alchemical transfer method (ATM) and the well-established potential of mean force (PMF) physical pathway method, using the same setup and force field model. The calculated binding free energies from the two methods are in excellent quantitative agreement. Importantly, the results from the two methods were also found to agree well with the experimental binding affinities released subsequently, with an $R^2$ of 0.89 (ATM) and 0.83 (PMF). Given that the two free energy methods are based on entirely different thermodynamic pathways, the close agreement between the results from the two methods and their general agreement with the experimental binding free energies are a testament to the high quality achieved by theory and methods. The study provides further validation of the novel ATM binding free energy estimation protocol and it paves the way to further extensions of the method to more complex systems.