# Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

arXiv: https://arxiv.org/abs/2307.07085 · HTML: https://mlsanity.com/paper/2307.07085/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 1 of 1)

- [Stochastic Aggregation in Graph Neural Networks](https://mlsanity.com/paper/2102.12648/) (2021)

## Built on by (most cited first, 5 of 5)

- [On the design space between molecular mechanics and machine learning force fields](https://mlsanity.com/paper/2409.01931/) (2024)
- [Grappa -- A Machine Learned Molecular Mechanics Force Field](https://mlsanity.com/paper/2404.00050/) (2024)
- [Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space Coverage](https://mlsanity.com/paper/2408.12817/) (2024)
- [Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective](https://mlsanity.com/paper/2410.18101/) (2024)
- [Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamics](https://mlsanity.com/paper/2604.25244/) (2026)

## Abstract (arXiv)

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, textttespaloma-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, textttespaloma-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.