ArticleJournal of chemical theory and computation2024
AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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Who cites it
7 citing papers in PubMed.
- Mapping Still Matters: Coarse-Graining with Machine Learning Potentials.Journal of chemical information and modeling · 2026Article
- Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
- Generation of protein dynamics by machine learning.Current opinion in structural biology · 2025Review
- Article
- mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics.Scientific data · 2024Article
- The Role of Neural Network Analysis in Identifying Predictors of Gastric Cancer.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2024Article
- Editorial: Revolutionizing life sciences: the nobel leap in artificial intelligence-driven biomodeling.Frontiers in molecular biosciences · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-force-field (AMARO), a new neural network potential (NNP) that combines an O(3)-equivariant message-passing neural network architecture, TensorNet, with a coarse-graining map that excludes hydrogen atoms. AMARO demonstrates the feasibility of training coarser NNP, without prior energy terms, to run stable protein dynamics with scalability and generalization capabilities.
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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.