ArticleACS central science2019
Machine Learning of Coarse-Grained Molecular Dynamics Force Fields.
Article in ACS central science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 156 papers.
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Who cites it
156 citing papers in PubMed.
- Building RNA coarse-grained force fields: Design principles and training strategies.Biophysical journal · 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
- Machine learning-enhanced nano-QSAR and multiscale modeling for predictive nanomedicine: applications in herbal therapeutics and neglected tropical diseases.Discover nano · 2026Review
- In Search of Shape in the Unshaped: Constructing Ensembles of Intrinsically Disordered Proteins.Biophysics reviews · 2026Article
- From possibility to precision in macromolecular ensemble prediction.Nature methods · 2026Article
- AI-based methods for simulating, sampling, and predicting protein ensembles.Current opinion in structural biology · 2026Review
- Probabilistic Forecasting for Coarse-Grained Molecular Dynamics.Journal of chemical theory and computation · 2026Article
- Active subspace learning for coarse-grained molecular dynamics.The Journal of chemical physics · 2026Article
- Transferable generative models bridge femtosecond to nanosecond time-step molecular dynamics.Science advances · 2026Article
- Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model.ArXiv · 2026Article
- Deep Learning-Guided Discovery of Dual Inhibitors of SARS-CoV-2 Entry and 3CL Protease.Molecules (Basel, Switzerland) · 2026Article
- Learning data-efficient coarse-grained molecular dynamics from forces and noise.Nature communications · 2026Article
- Mapping Still Matters: Coarse-Graining with Machine Learning Potentials.Journal of chemical information and modeling · 2026Article
- Quantum vibrational spectroscopy with classical trajectories.Chemical science · 2026Review
- Review
- Resolving Conformational Heterogeneity in Intrinsically Disordered Proteins via Experimentally Guided Multi-Replica Simulations.bioRxiv : the preprint server for biology · 2026Article
- Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.Biophysical journal · 2026Review
- Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
- Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data.Journal of chemical theory and computation · 2026Article
96 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force-matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface. Thus, CGnets are able to capture multibody terms that emerge from the dimensionality reduction.
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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.