High-Precision Solvation Free Energy Calculation via Multi-Input Linear Correction in 3D-RISM Theory

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Authors: Yutaka Maruyama, Norio Yoshida
Cited by: 3
References: 96

Abstract

This study introduces the multi-input linear correction (MILC) method to enhance the accuracy of solvation free energy (SFE) calculations with the three-dimensional reference interaction site model (3D-RISM) theory. While the 3D-RISM theory offers significant computational efficiency compared with molecular simulation-based methods, conventional energy functionals─such as the Singer-Chandler or Gaussian fluctuation formulas─often suffer from systematic overestimation of SFEs. In general, partial molar volume (PMV) corrections are employed to account for errors related to hydrophobic and cavity formation energies. To further improve the prediction accuracy, the MILC method employs a physically motivated data-driven approach that incorporates not only the standard SFE components but also physical quantities obtained when the solute atomic charges are set to zero. Accordingly, the method requires two independent 3D-RISM calculations per solute. The robustness and transferability of the MILC method were rigorously validated using a nested cross-validation protocol on 628 molecules in the FreeSolv database (excluding carboxylic acids). By averaging over 10 conformers per molecule, the method achieved a mean absolute deviation (MAD) of 0.38 kcal/mol relative to the benchmark Bennett acceptance ratio (BAR) method. Furthermore, we demonstrate that the MILC method outperforms conventional volume-based corrections and provides predictive performance superior or comparable to complex machine learning models while maintaining high physical interpretability. This level of accuracy and statistical reliability demonstrates that the 3D-RISM theory with the MILC method is a robust and efficient tool for high-throughput applications in drug discovery and material design.

Fields of Study & Subjects

Computational Drug Discovery Methods Protein Structure and Dynamics Machine Learning in Materials Science

Publication details

Journal / source
Journal of Chemical Theory and Computation
Publisher
American Chemical Society (ACS)
Volume
22
Issue
7
Pages
3596–3612
Publication date
24 March 2026
Language
EN