ArticleJournal of chemical theory and computation2022
Contrastive Learning of Coarse-Grained Force Fields.
Article in Journal of chemical theory and computation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model.Journal of chemical theory and computation · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- MOFF2: A Transferable Coarse-Grained Protein Force Field for Predictive Condensate Simulations.bioRxiv : the preprint server for biology · 2026Article
- NEAT-DNA: A Chemically Accurate, Sequence-Dependent Coarse-Grained Model for Large-Scale DNA Simulations.Journal of chemical theory and computation · 2026Article
- Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model.ArXiv · 2026Article
- Learning data-efficient coarse-grained molecular dynamics from forces and noise.Nature communications · 2026Article
- StrEAMM-Thioether: Efficient Structure Prediction for Thioether-Linked Cyclic Peptides.The journal of physical chemistry. B · 2026Article
- Sequence-Dependent Conformational Landscapes of Intrinsically Disordered Proteins Reveal Asymmetric Chain Compaction.Journal of chemical theory and computation · 2025Article
- Peering inside the black box by learning the relevance of many-body functions in neural network potentials.Nature communications · 2025Article
- NEAT-DNA: A Chemically Accurate, Sequence-Dependent Coarse-Grained Model for Large-Scale DNA Simulations.bioRxiv : the preprint server for biology · 2025Article
- Navigating protein landscapes with a machine-learned transferable coarse-grained model.Nature chemistry · 2025Article
- Toward Predictive Coarse-Grained Simulations of Biomolecular Condensates.Biochemistry · 2025Review
- Scaling Graph Neural Networks to Large Proteins.Journal of chemical theory and computation · 2025Article
- On the emergence of machine-learning methods in bottom-up coarse-graining.Current opinion in structural biology · 2025Review
- Machine learning in biological physics: From biomolecular prediction to design.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks.ACS central science · 2023Article
- Transferable Coarse Graining via Contrastive Learning of Graph Neural Networks.bioRxiv : the preprint server for biology · 2023Article
- OpenABC enables flexible, simplified, and efficient GPU accelerated simulations of biomolecular condensates.PLoS computational biology · 2023Article
- OpenABC Enables Flexible, Simplified, and Efficient GPU Accelerated Simulations of Biomolecular Condensates.bioRxiv : the preprint server for biology · 2023Article
- Machine learned coarse-grained protein force-fields: Are we there yet?Current opinion in structural biology · 2023Review
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2 authors.
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Abstract
Coarse-grained models have proven helpful for simulating complex systems over long time scales to provide molecular insights into various processes. Methodologies for systematic parametrization of the underlying energy function or force field that describes the interactions among different components of the system are of great interest for ensuring simulation accuracy. We present a new method, potential contrasting, to enable efficient learning of force fields that can accurately reproduce the conformational distribution produced with all-atom simulations. Potential contrasting generalizes the noise contrastive estimation method with umbrella sampling to better learn the complex energy landscape of molecular systems. When applied to the Trp-cage protein, we found that the technique produces force fields that thoroughly capture the thermodynamics of the folding process despite the use of only α-carbons in the coarse-grained model. We further showed that potential contrasting could be applied over large data sets that combine the conformational ensembles of many proteins to improve force field transferability. We anticipate potential contrasting as a powerful tool for building general-purpose coarse-grained force fields.
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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.