Machine-learned molecular mechanics force fields from large-scale quantum chemical data Journal Article
| Authors: | Takaba, K.; Friedman, A. J.; Cavender, C. E.; Behara, P. K.; Pulido, I.; Henry, M. M.; MacDermott-Opeskin, H.; Iacovella, C. R.; Nagle, A. M.; Payne, A. M.; Shirts, M. R.; Mobley, D. L.; Chodera, J. D.; Wang, Y. |
| Article Title: | Machine-learned molecular mechanics force fields from large-scale quantum chemical data |
| Abstract: | The development of reliable and extensible molecular mechanics (MM) force fields—fast, empirical models characterizing the potential energy surface of molecular systems—is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1 M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides and folded proteins, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest. © 2024 The Royal Society of Chemistry. |
| Keywords: | molecular mechanics; nucleic acids; peptides; binding energy; small molecules; quantum chemistry; potential energy; large datasets; forcefields; quantum chemical; graph neural networks; biomolecular simulation; large-scales; chemical data; chemical domain; empirical model; molecular systems; potential-energy surfaces |
| Journal Title: | Chemical Science |
| Volume: | 15 |
| Issue: | 32 |
| ISSN: | 2041-6520 |
| Publisher: | Royal Society of Chemistry |
| Date Published: | 2024-08-28 |
| Start Page: | 12861 |
| End Page: | 12878 |
| Language: | English |
| DOI: | 10.1039/d4sc00690a |
| PROVIDER: | scopus |
| PMCID: | PMC11322960 |
| PUBMED: | 39148808 |
| DOI/URL: | |
| Notes: | Article -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PubMed and PDF -- MSK corresponding author is John Chodera -- Source: Scopus |
Altmetric
Citation Impact
BMJ Impact Analytics
Related MSK Work