Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space Coverage
This provides a scalable tool for computational drug discovery, addressing the limitations of traditional methods in covering expansive chemical spaces, though it is incremental as it builds on existing GNN and force field frameworks.
The authors tackled the challenge of accurately parametrizing molecular mechanics force fields for drug-like molecules across a broad chemical space by developing ByteFF, a data-driven force field that predicts all bonded and non-bonded parameters simultaneously, achieving state-of-the-art performance on benchmarks with high accuracy in geometry and energy predictions.
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.