ArticleJournal of the American Chemical Society2025
Rapid Access to Small Molecule Conformational Ensembles in Organic Solvents Enabled by Graph Neural Network-Based Implicit Solvent Model.
Article in Journal of the American Chemical Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- 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
- Graph-Based Machine Learning Identifies Oxygenated Block Polymer Replacements for Conventional Plastics and Elastics.Journal of the American Chemical Society · 2026Article
- Unraveling Torsional Preferences: Comparative Analysis of Torsion Motif Torsional-Angle Distributions across Different Environments.Journal of chemical information and modeling · 2025Article
- Graph neural networks driven acceleration in drug discovery.Acta pharmaceutica Sinica. B · 2025Review
- 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
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding and manipulating the conformational behavior of a molecule in different solvent environments is of great interest in the fields of drug discovery and organic synthesis. Molecular dynamics (MD) simulations with solvent molecules explicitly present are the gold standard to compute such conformational ensembles (within the accuracy of the underlying force field), complementing experimental findings and supporting their interpretation. However, conventional methods often face challenges related to computational cost (explicit solvent) or accuracy (implicit solvent). Here, we showcase how our graph neural network (GNN)-based implicit solvent (GNNIS) approach can be used to rapidly compute small molecule conformational ensembles in 39 common organic solvents reproducing explicit-solvent simulations with high accuracy. We validate this approach using nuclear magnetic resonance (NMR) measurements, thus identifying the conformers contributing most to the experimental observable. The method allows the time required to accurately predict conformational ensembles to be reduced from days to minutes while achieving results within one
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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.