ArticleChemical science2024
Machine-learned molecular mechanics force fields from large-scale quantum chemical data.
Article in Chemical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Training a force field for proteins and small molecules from scratch.Chemical science · 2026Article
- Machine Learning Approach to Estimating Wall-Clock Times and Assigning Resources for a Wide Variety of Quantum Chemical Calculations.ACS omega · 2026Article
- Integrating Coupled-Cluster Theory Within AI Workflows for Accurate Molecular Geometries.Journal of computational chemistry · 2026Article
- Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model.Journal of chemical theory and computation · 2026Article
- The Future of Foundation Machine Learning Potentials and DFT in Homogeneous Catalysis: Competition or Synergy?Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Deep residual learning for molecular force fields.Nature communications · 2026Article
- Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model.ArXiv · 2026Article
- A Hooker Oxygenase Archetype in Polyketide Biosynthesis Challenging the Baeyer-Villiger Monooxygenase Paradigm.Journal of the American Chemical Society · 2026Article
- Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.Biophysical journal · 2026Review
- AlphaFold-RandomWalk and AlphaFold-Ensemble: Sampling Alternative Protein Conformations with Perturbed Versions of AlphaFold.Journal of chemical information and modeling · 2026Article
- HessFit: Deriving Molecule-Specific Force Fields from Quantum Mechanical Hessians.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Condensation of Force Field Parameters from Machine Learning Predicted Distributions for High-Throughput Virtual Screening Applications.Journal of chemical information and modeling · 2025Article
- Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications.Biophysical reviews · 2025Review
- How many crystal structures do you need to trust your docking results?bioRxiv : the preprint server for biology · 2025Article
- Reversible molecular simulation for training classical and machine-learning force fields.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Toward Predictive Coarse-Grained Simulations of Biomolecular Condensates.Biochemistry · 2025Review
- How does machine learning augment alchemical binding free energy calculations?Future medicinal chemistry · 2025Article
- Scaling Graph Neural Networks to Large Proteins.Journal of chemical theory and computation · 2025Article
- Data-driven parametrization of molecular mechanics force fields for expansive chemical space coverage.Chemical science · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.