ABCG2: A Milestone Charge Model for Accurate Solvation Free Energy Calculation

In this report, we describe the development and validation of ABCG2, a new charge model with milestone free energy accuracy, while allowing instantaneous atomic charge assignment for arbitrary organic molecules. In combination with the second-generation general AMBER force field (GAFF2), ABCG2 led to a root-mean-square error (RMSE) of 0.99 kcal/mol on the hydration free energy calculation of all 642 solutes in the FreeSolv database, for the first time meeting the chemical accuracy threshold through physics-based molecular simulation against the golden-standard data set. Against the Minnesota Solvation Database, the solvation free energy calculation on 2068 pairs of a range of organic solutes in diverse solvents led to an RMSE of 0.89 kcal/mol. The 1913 data points of transfer free energies from the aqueous solution to organic solvents obtained an RMSE of 0.85 kcal/mol, corresponding to 0.63 log units for logP. The benchmark on densities of neat liquids for 1839 organic molecules and heat of vaporizations of 874 organic liquids achieved a comparable performance with the default restrained electrostatic potential (RESP) charge method of GAFF2. The fluctuations of assigned partial atomic charges over different input conformations from ABCG2 are demonstrated to be much smaller than those of RESP from statistics of 96 real drug molecules. The validation results demonstrated not only the accuracy but also the transferability and generality of the GAFF2/ABCG2 combination.

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Publication Details

Journal
Journal of Chemical Theory and Computation
Published
2025-03-11
DOI
https://doi.org/10.1021/acs.jctc.5c00038
Citations
10
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
5.18

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article

ABCG2: A Milestone Charge Model for Accurate Solvation Free Energy Calculation

Xibing He, Wei Yang, Tai‐Sung Lee, Viet Hoang Man et al.
10 citations
Journal of Chemical Theory and Computation
Machine Learning in Materials Science
5.18
article

ABCG2: A Milestone Charge Model for Accurate Solvation Free Energy Calculation

Xibing He, Wei Yang, Tai‐Sung Lee, Viet Hoang Man, Junmei Wang
article en
10 citations

Abstract

In this report, we describe the development and validation of ABCG2, a new charge model with milestone free energy accuracy, while allowing instantaneous atomic charge assignment for arbitrary organic molecules. In combination with the second-generation general AMBER force field (GAFF2), ABCG2 led to a root-mean-square error (RMSE) of 0.99 kcal/mol on the hydration free energy calculation of all 642 solutes in the FreeSolv database, for the first time meeting the chemical accuracy threshold through physics-based molecular simulation against the golden-standard data set. Against the Minnesota Solvation Database, the solvation free energy calculation on 2068 pairs of a range of organic solutes in diverse solvents led to an RMSE of 0.89 kcal/mol. The 1913 data points of transfer free energies from the aqueous solution to organic solvents obtained an RMSE of 0.85 kcal/mol, corresponding to 0.63 log units for logP. The benchmark on densities of neat liquids for 1839 organic molecules and heat of vaporizations of 874 organic liquids achieved a comparable performance with the default restrained electrostatic potential (RESP) charge method of GAFF2. The fluctuations of assigned partial atomic charges over different input conformations from ABCG2 are demonstrated to be much smaller than those of RESP from statistics of 96 real drug molecules. The validation results demonstrated not only the accuracy but also the transferability and generality of the GAFF2/ABCG2 combination.

Journal of Chemical Theory and ComputationVol. 21(6)
Rutgers, The State University of New Jersey (US), Florida State University (US), University of Pittsburgh (US), Rutgers Sexual and Reproductive Health and Rights (NL), Genomics (United Kingdom) (GB)
National Science Foundation, National Institute on Aging, National Institute of General Medical Sciences
Affordable and clean energy
Openalex Percentile: Top 4%
Machine Learning in Materials Science
5.18
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