Extending machine learning model for implicit solvation to free energy calculations

TL;DR

This paper introduces LSNN, a graph neural network-based implicit solvent model trained on 300,000 molecules, enabling accurate free energy predictions comparable to explicit models with faster computation.

Contribution

The novel LSNN model incorporates derivatives of alchemical variables in training, improving free energy calculation accuracy over force-matching alone.

Findings

LSNN achieves free energy prediction accuracy comparable to explicit-solvent simulations.

The model provides a significant computational speedup for thermodynamic calculations.

Training on 300,000 molecules demonstrates scalability and robustness.

Abstract

The implicit solvent approach offers a computationally efficient framework to model solvation effects in molecular simulations. However, its accuracy often falls short compared to explicit solvent models, limiting its use in precise thermodynamic calculations. Recent advancements in machine learning (ML) present an opportunity to overcome these limitations by leveraging neural networks to develop more precise implicit solvent potentials for diverse applications. A major drawback of current ML-based methods is their reliance on force-matching alone, which can lead to energy predictions that differ by an arbitrary constant and are therefore unsuitable for absolute free energy comparisons. Here, we introduce a novel methodology with a graph neural network (GNN)-based implicit solvent model, dubbed Lambda Solvation Neural Network (LSNN). In addition to force-matching, this network was trained to match the derivatives of alchemical variables, ensuring that solvation free energies can be meaningfully compared across chemical species. Trained on a dataset of approximately 300,000 small molecules, LSNN achieves free energy predictions with accuracy comparable to explicit-solvent alchemical simulations, while offering a computational speedup and establishing a foundational framework for future applications in drug discovery.