ReviewCurrent opinion in structural biology2023
Machine learned coarse-grained protein force-fields: Are we there yet?
Review in Current opinion in structural biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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.
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Who cites it
33 citing papers in PubMed.
- Soft Computing Approaches toward Density-Based Analysis of Many-Electron Systems.ACS physical chemistry Au · 2026Review
- Methods for the establishment of enzymatic mechanisms - from QM to ML.Chemical science · 2026Review
- NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.Journal of chemical theory and computation · 2026Article
- Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model.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
- Bayesian Optimization for High-Dimensional Coarse-Grained Model Parameterization: A Case Study on Pebax Polymer.Journal of chemical theory and computation · 2026Article
- Mapping Still Matters: Coarse-Graining with Machine Learning Potentials.Journal of chemical information and modeling · 2026Article
- Extending the range of graph neural networks with global encodings.Nature communications · 2026Article
- Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
- Development of Coarse-Grained Lipid Force Fields Based on a Graph Neural Network.Journal of chemical theory and computation · 2025Article
- Generation of protein dynamics by machine learning.Current opinion in structural biology · 2025Review
- Navigating protein landscapes with a machine-learned transferable coarse-grained model.Nature chemistry · 2025Article
- Deep learning-guided design of dynamic proteins.Science (New York, N.Y.) · 2025Article
- Martini 3 coarse-grained model of enzymes: Framework with validation by all-atom simulations and x-ray diffraction measurements.The Journal of chemical physics · 2025Article
- Scaling Graph Neural Networks to Large Proteins.Journal of chemical theory and computation · 2025Article
- Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023.Chemical science · 2025Article
- Prediction rigidities for data-driven chemistry.Faraday discussions · 2025Article
- AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics.Journal of chemical theory and computation · 2024Article
- Embracing exascale computing in nucleic acid simulations.Current opinion in structural biology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
The successful recent application of machine learning methods to scientific problems includes the learning of flexible and accurate atomic-level force-fields for materials and biomolecules from quantum chemical data. In parallel, the machine learning of force-fields at coarser resolutions is rapidly gaining relevance as an efficient way to represent the higher-body interactions needed in coarse-grained force-fields to compensate for the omitted degrees of freedom. Coarse-grained models are important for the study of systems at time and length scales exceeding those of atomistic simulations. However, the development of transferable coarse-grained models via machine learning still presents significant challenges. Here, we discuss recent developments in this field and current efforts to address the remaining challenges.
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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.