ArticleNature communications2022
Accurate determination of solvation free energies of neutral organic compounds from first principles.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Accurate Solution-Phase Thermodynamics via Composite Quantum Chemistry and Machine Learning: Application to Biomass Decomposition.Journal of computational chemistry · 2026Article
- Deep residual learning for molecular force fields.Nature communications · 2026Article
- 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
- Using Deep Graph Neural Networks Improves Physics-Based Hydration Free Energy Predictions Even for Molecules Outside of the Training Set Distribution.The journal of physical chemistry. B · 2025Article
- The Q-AMOEBA (CF) Polarizable Potential.The journal of physical chemistry letters · 2025Article
- Structure-Property Relevance of Two Pairs of Isomeric Steviol Rebaudiosides and the Underlying Mechanism.Foods (Basel, Switzerland) · 2025Article
- ABCG2: A Milestone Charge Model for Accurate Solvation Free Energy Calculation.Journal of chemical theory and computation · 2025Article
- The Determination of Free Energy of Hydration of Water Ions from First Principles.Journal of chemical theory and computation · 2024Article
- Inclusion of Water Multipoles into the Implicit Solvation Framework Leads to Accuracy Gains.The journal of physical chemistry. B · 2024Article
- Explainable Supervised Machine Learning Model To Predict Solvation Gibbs Energy.Journal of chemical information and modeling · 2024Article
- Differentiable simulation to develop molecular dynamics force fields for disordered proteins.Chemical science · 2024Article
- Neural Network Corrections to Intermolecular Interaction Terms of a Molecular Force Field Capture Nuclear Quantum Effects in Calculations of Liquid Thermodynamic Properties.Journal of chemical theory and computation · 2024Article
- Combining Force Fields and Neural Networks for an Accurate Representation of Bonded Interactions.The journal of physical chemistry. A · 2024Article
- Improving the Accuracy of Physics-Based Hydration-Free Energy Predictions by Machine Learning the Remaining Error Relative to the Experiment.Journal of chemical theory and computation · 2024Article
- Force-field-enhanced neural network interactions: from local equivariant embedding to atom-in-molecule properties and long-range effects.Chemical science · 2023Article
- Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions.Journal of the American Chemical Society · 2023Article
- Solvation thermodynamics from cavity shapes of amino acids.PNAS nexus · 2023Article
- Protein-Ligand Binding Free-Energy Calculations with ARROW─A Purely First-Principles Parameterized Polarizable Force Field.Journal of chemical theory and computation · 2022Article
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Authors and funding
25 authors.
Funding
Abstract
The main goal of molecular simulation is to accurately predict experimental observables of molecular systems. Another long-standing goal is to devise models for arbitrary neutral organic molecules with little or no reliance on experimental data. While separately these goals have been met to various degrees, for an arbitrary system of molecules they have not been achieved simultaneously. For biophysical ensembles that exist at room temperature and pressure, and where the entropic contributions are on par with interaction strengths, it is the free energies that are both most important and most difficult to predict. We compute the free energies of solvation for a diverse set of neutral organic compounds using a polarizable force field fitted entirely to ab initio calculations. The mean absolute errors (MAE) of hydration, cyclohexane solvation, and corresponding partition coefficients are 0.2 kcal/mol, 0.3 kcal/mol and 0.22 log units, i.e. within chemical accuracy. The model (ARROW FF) is multipolar, polarizable, and its accompanying simulation stack includes nuclear quantum effects (NQE). The simulation tools' computational efficiency is on a par with current state-of-the-art packages. The construction of a wide-coverage molecular modelling toolset from first principles, together with its excellent predictive ability in the liquid phase is a major advance in biomolecular simulation.
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