Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space Coverage

TL;DR

This paper introduces ByteFF, a data-driven, GNN-based force field for molecular dynamics that covers broad chemical space with high accuracy, enhancing computational drug discovery.

Contribution

Developed ByteFF, a novel Amber-compatible force field trained on a large, diverse dataset, enabling accurate predictions across expansive chemical space.

Findings

ByteFF achieves state-of-the-art accuracy on benchmark datasets.

It predicts force field parameters for diverse drug-like molecules.

ByteFF demonstrates high accuracy in geometries, torsions, and energies.

Abstract

A force field is a critical component in molecular dynamics simulations for computational drug discovery. It must achieve high accuracy within the constraints of molecular mechanics' (MM) limited functional forms, which offers high computational efficiency. With the rapid expansion of synthetically accessible chemical space, traditional look-up table approaches face significant challenges. In this study, we address this issue using a modern data-driven approach, developing ByteFF, an Amber-compatible force field for drug-like molecules. To create ByteFF, we generated an expansive and highly diverse molecular dataset at the B3LYP-D3(BJ)/DZVP level of theory. This dataset includes 2.4 million optimized molecular fragment geometries with analytical Hessian matrices, along with 3.2 million torsion profiles. We then trained an edge-augmented, symmetry-preserving molecular graph neural network (GNN) on this dataset, employing a carefully optimized training strategy. Our model predicts all bonded and non-bonded MM force field parameters for drug-like molecules simultaneously across a broad chemical space. ByteFF demonstrates state-of-the-art performance on various benchmark datasets, excelling in predicting relaxed geometries, torsional energy profiles, and conformational energies and forces. Its exceptional accuracy and expansive chemical space coverage make ByteFF a valuable tool for multiple stages of computational drug discovery.