ArticleThe journal of physical chemistry. B2024
Insights and Challenges in Correcting Force Field Based Solvation Free Energies Using a Neural Network Potential.
Article in The journal of physical chemistry. B, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Enabling Biomolecular Simulations with Neural Network Potentials in GROMACS.Journal of chemical theory and computation · 2026Article
- Dual-Coordinate Relative Free Energy Simulations Using Machine-Learned Interatomic Potentials.Journal of chemical theory and computation · 2026Article
- Computing Solvation Free Energies of Small Molecules with Experimental Accuracy.Journal of the American Chemical Society · 2026Article
- Repeatability of Relative Free Energy Calculations in Solution with ANI-2x and MACE-OFF23.Journal of chemical theory and computation · 2026Article
- Data-Efficient Equivariant NNPs Enable DFT-Accurate Simulations and Implicit Solvation Free Energies.The journal of physical chemistry. B · 2025Article
- Architecture-Independent Absolute Solvation Free Energy Calculations with Neural Network Potentials.The journal of physical chemistry letters · 2025Article
- Leveraging Molecular Mechanics With the uESE Continuum Solvation Model for Efficient Solvation Free Energy Prediction: Impact of Conformation and Extensive Validation.Journal of computational chemistry · 2025Article
- Transferable Neural Network Potentials and Condensed Phase Properties.Journal of chemical information and modeling · 2025Article
- Quantification of the Impact of Structure Quality on Predicted Binding Free Energy Accuracy.Journal of chemical information and modeling · 2025Article
- TrIP2: Expanding the Transformer Interatomic Potential Demonstrates Architectural Scalability for Organic Compounds.The journal of physical chemistry. A · 2025Article
- ABCG2: A Milestone Charge Model for Accurate Solvation Free Energy Calculation.Journal of chemical theory and computation · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
We present a comprehensive study investigating the potential gain in accuracy for calculating absolute solvation free energies (ASFE) using a neural network potential to describe the intramolecular energy of the solute. We calculated the ASFE for most compounds from the FreeSolv database using the Open Force Field (OpenFF) and compared them to earlier results obtained with the CHARMM General Force Field (CGenFF). By applying a nonequilibrium (NEQ) switching approach between the molecular mechanics (MM) description (either OpenFF or CGenFF) and the neural net potential (NNP)/MM level of theory (using ANI-2x as the NNP potential), we attempted to improve the accuracy of the calculated ASFEs. The predictive performance of the results did not change when this approach was applied to all 589 small molecules in the FreeSolv database that ANI-2x can describe. When selecting a subset of 156 molecules, focusing on compounds where the force fields performed poorly, we saw a slight improvement in the root-mean-square error (RMSE) and mean absolute error (MAE). The majority of our calculations utilized unidirectional NEQ protocols based on Jarzynski's equation. Additionally, we conducted bidirectional NEQ switching for a subset of 156 solutes. Notably, only a small fraction (10 out of 156) exhibited statistically significant discrepancies between unidirectional and bidirectional NEQ switching free energy estimates.
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Registered trials
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