ArticleJournal of chemical information and modeling2025
DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Enabling Biomolecular Simulations with Neural Network Potentials in GROMACS.Journal of chemical theory and computation · 2026Article
- Article
- Automated Force Field Developer and Optimizer Platform: Torsion Reparameterization.Journal of chemical information and modeling · 2026Article
- OMetaNet: an efficient hybrid deep learning model based on multimodal data fusion and contrastive learning for predicting 2'-O-methylation sites in human RNA.BMC bioinformatics · 2025Article
- Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications.Biophysical reviews · 2025Review
- Molecular dynamics simulations of proteins: an in-depth review of computational strategies, structural insights, and their role in medicinal chemistry and drug development.Biological cybernetics · 2025Review
- chemtrain-deploy: A Parallel and Scalable Framework for Machine Learning Potentials in Million-Atom MD Simulations.Journal of chemical theory and computation · 2025Article
- Accelerating Data Set Population for Training Machine Learning Potentials with Automated System Generation and Strategic Sampling.Journal of chemical theory and computation · 2025Article
- FE-ToolKit: A Versatile Software Suite for Analysis of High-Dimensional Free Energy Surfaces and Alchemical Free Energy Networks.Journal of chemical information and modeling · 2025Article
- DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials.Journal of chemical theory and computation · 2025Article
- Strategies for the drug development of cancer therapeutics.Frontiers in pharmacology · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Machine learning potentials (MLPs) have revolutionized molecular simulation by providing efficient and accurate models for predicting atomic interactions. MLPs continue to advance and have had profound impact in applications that include drug discovery, enzyme catalysis, and materials design. The current landscape of MLP software presents challenges due to the limited interoperability between packages, which can lead to inconsistent benchmarking practices and necessitates separate interfaces with molecular dynamics (MD) software. To address these issues, we present DeePMD-GNN, a plugin for the DeePMD-kit framework that extends its capabilities to support external graph neural network (GNN) potentials.DeePMD-GNN enables the seamless integration of popular GNN-based models, such as NequIP and MACE, within the DeePMD-kit ecosystem. Furthermore, the new software infrastructure allows GNN models to be used within combined quantum mechanical/molecular mechanical (QM/MM) applications using the range corrected ΔMLP formalism.We demonstrate the application of DeePMD-GNN by performing benchmark calculations of NequIP, MACE, and DPA-2 models developed under consistent training conditions to ensure fair comparison.
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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.