{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/espaloma-0-3-0-machine-learned-molecular","title":"Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond","arxiv_id":"2307.07085","date":"2023-07-13","proceeding":null,"authors":["Kenichiro Takaba","Iván Pulido","Pavan Kumar Behara","Chapin E. Cavender","Anika J. Friedman","Michael M. Henry","Hugo MacDermott Opeskin","Christopher R. Iacovella","Arnav M. Nagle","Alexander Matthew Payne","Michael R. Shirts","David L. Mobley","John D. Chodera","Yuanqing Wang"],"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, \\texttt{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 1.1M energy and force calculations, \\texttt{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, 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.","url_abs":"https://arxiv.org/abs/2307.07085v4","url_pdf":"https://arxiv.org/pdf/2307.07085v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"espaloma-0-3-0-machine-learned-molecular","repo_url":"https://github.com/choderalab/espaloma-0.3.0-manuscript","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"espaloma-0-3-0-machine-learned-molecular","repo_url":"https://github.com/choderalab/geometry-benchmark-espaloma","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"espaloma-0-3-0-machine-learned-molecular","repo_url":"https://github.com/choderalab/pl-benchmark-espaloma-experiment","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"espaloma-0-3-0-machine-learned-molecular","repo_url":"https://github.com/choderalab/refit-espaloma","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"espaloma-0-3-0-machine-learned-molecular","repo_url":"https://github.com/choderalab/vanilla-espaloma-experiment","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"espaloma-0-3-0-machine-learned-molecular","repo_url":"https://github.com/hits-mbm-dev/grappa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"espaloma-0-3-0-machine-learned-molecular","repo_url":"https://github.com/openmm/openmmforcefields","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"drug-design","task_name":"Drug Design"},{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":null,"task_name":"GPU"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}