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Improving the Accuracy of Physics-Based Hydration-Free Energy Predictions by Machine Learning the Remaining Error
Lewis Bass1, Luke H Elder2, Dan E Folescu2,3
1Department of Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.
Journal of Chemical Theory and Computation
|December 27, 2023
Summary
Machine learning (ML) enhances computational water models by correcting errors in physics-based simulations. This approach improves hydration-free energy predictions for biomolecules with minimal computational cost.
Area of Science:
- Computational chemistry
- Biomolecular simulations
- Machine learning applications
Background:
- Accurate computational models of water are crucial for atomistic simulations of biomolecules.
- Predicting hydration-free energies (HFEs) is essential for understanding molecular interactions in aqueous environments.
Purpose of the Study:
- To develop a computationally efficient method to improve the accuracy of hydration-free energy predictions.
- To mitigate remaining errors in physics-based models using machine learning as a postprocessing step.
Main Methods:
- A graph convolutional neural network was trained to identify and correct "blind spots" in physics-based models.
- The strategy was tested on five classical solvent models (e.g., generalized Born, TIP3P) using experimental HFEs from the FreeSolv dataset.
Main Results:
- Machine learning correction reduced the root-mean-square error for HFEs across all tested models.
- Accuracy improvements ranged from 20% to 47%, with ML-corrected TIP3P achieving below 1 kcal/mol accuracy.
- The ML approach showed minimal overfitting and negligible computational overhead.
Conclusions:
- The proposed ML strategy effectively learns and mitigates remaining errors in physics-based hydration models.
- This method offers a significant advantage over training ML models directly on reference HFEs, preserving overall trends.
- The approach enhances the accuracy of various water models, making them more reliable for biomolecular simulations.
