ArticleACS central science2023
Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks.
Article in ACS central science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model.Journal of chemical theory and computation · 2026Article
- Development and Test of Highly Accurate End Point Free Energy Methods. 4. Expanding Solvents Capability and logBB Prediction.The journal of physical chemistry. B · 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
- Binding Free Energies without Alchemy.ArXiv · 2026Article
- Learning data-efficient coarse-grained molecular dynamics from forces and noise.Nature communications · 2026Article
- Sequence-Dependent Conformational Landscapes of Intrinsically Disordered Proteins Reveal Asymmetric Chain Compaction.Journal of chemical theory and computation · 2025Article
- NEAT-DNA: A Chemically Accurate, Sequence-Dependent Coarse-Grained Model for Large-Scale DNA Simulations.bioRxiv : the preprint server for biology · 2025Article
- Transferring Knowledge from MM to QM: A Graph Neural Network-Based Implicit Solvent Model for Small Organic Molecules.Journal of chemical theory and computation · 2025Article
- Navigating protein landscapes with a machine-learned transferable coarse-grained model.Nature chemistry · 2025Article
- Rapid Access to Small Molecule Conformational Ensembles in Organic Solvents Enabled by Graph Neural Network-Based Implicit Solvent Model.Journal of the American Chemical Society · 2025Article
- Toward Predictive Coarse-Grained Simulations of Biomolecular Condensates.Biochemistry · 2025Review
- Chemically Informed Coarse-Graining of Electrostatic Forces in Charge-Rich Biomolecular Condensates.ACS central science · 2025Article
- 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
- ChromoGen: Diffusion model predicts single-cell chromatin conformations.Science advances · 2025Article
- Thermodynamic Transferability in Coarse-Grained Force Fields Using Graph Neural Networks.Journal of chemical theory and computation · 2024Article
- A general graph neural network based implicit solvation model for organic molecules in water.Chemical science · 2024Article
- Differentiable simulation to develop molecular dynamics force fields for disordered proteins.Chemical science · 2024Article
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3 authors.
Funding
Abstract
Implicit solvent models are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. Efforts are underway to develop accurate and transferable implicit solvent models and coarse-grained (CG) force fields in general, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to the lack of analytical expressions for the PMF and algorithmic limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent the solvation free energy and potential contrasting for parameter optimization. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside of the training data. Our study offers valuable insights for deriving systematically improvable implicit solvent models and CG force fields from a bottom-up perspective.
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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.