A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning

TL;DR

This paper introduces an automated, scalable method for molecular force field parameterization that leverages DFT and neural network potentials, significantly reducing time and improving accuracy for drug discovery applications.

Contribution

The authors develop a novel automated parameterization approach using neural network potentials, enabling faster and more accurate force field generation compared to traditional DFT methods.

Findings

NNP-based parameterization is faster than DFT-based methods.

The method produces more accurate parameters than GAFF2.

Applicable to small molecules in drug discovery contexts.

Abstract

Fast and accurate molecular force field (FF) parameterization is still an unsolved problem. Accurate FFs are not generally available for all molecules, like novel druglike molecules. While methods based on quantum mechanics (QM) exist to parameterize them with better accuracy, they are computationally expensive and slow, which limits applicability to a small number of molecules. Here, we present an automated FF parameterization method which can utilize either DFT calculations or approximate QM energies produced by different neural network potentials (NNPs), to obtain improved parameters for molecules. We demonstrate that for the case of torchani-ANI-1x NNP, we can parameterize small molecules in a fraction of time compared with an equivalent parameterization using DFT QM calculations while producing more accurate parameters than FF (GAFF2). We expect our method to be of critical importance in computational structure-based drug discovery. The current version is available at PlayMolecule (www.playmolecule.org) and implemented in HTMD, allowing to parameterize molecules with different QM and NNP options.