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Insights and Challenges in Correcting Force Field Based Solvation Free Energies Using a Neural Network Potential.

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We explored using neural network potentials (NNP) with molecular mechanics (MM) to improve absolute solvation free energy (ASFE) calculations. While overall accuracy didn't significantly change, a subset of challenging molecules showed slight improvements in error metrics.

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Area of Science:

  • Computational Chemistry
  • Physical Chemistry
  • Molecular Modeling

Background:

  • Accurate calculation of absolute solvation free energies (ASFE) is crucial for understanding chemical processes.
  • Traditional molecular mechanics (MM) force fields may have limitations in describing solute intramolecular energies.
  • Neural network potentials (NNP) offer a data-driven approach to model complex molecular interactions.

Purpose of the Study:

  • To investigate the potential accuracy gains in ASFE calculations by incorporating a neural network potential (NNP) for solute intramolecular energy.
  • To compare the performance of the Open Force Field (OpenFF) and CHARMM General Force Field (CGenFF) in ASFE calculations.
  • To evaluate the effectiveness of nonequilibrium (NEQ) switching methods combined with NNP/MM for improving ASFE prediction.

Main Methods:

  • Calculated ASFE for compounds in the FreeSolv database using OpenFF and CGenFF.
  • Employed a nonequilibrium (NEQ) switching approach, combining MM with the ANI-2x NNP.
  • Utilized Jarzynski's equation for unidirectional NEQ switching, with bidirectional NEQ switching performed on a subset.

Main Results:

  • No significant change in predictive performance for ASFE calculations across the entire FreeSolv database (589 molecules).
  • A slight improvement in root-mean-square error (RMSE) and mean absolute error (MAE) was observed for a subset of 156 molecules where force fields previously performed poorly.
  • Statistically significant discrepancies between unidirectional and bidirectional NEQ switching were found in only a small fraction (10/156) of solutes.

Conclusions:

  • Integrating NNPs with MM does not universally enhance ASFE calculation accuracy for all molecules.
  • NNP/MM approaches show potential for improving ASFE predictions for specific challenging compounds.
  • Unidirectional NEQ switching provides a reliable estimation of ASFE, with bidirectional switching yielding similar results for most solutes.