ArticleCommunications chemistry2020
Instantaneous generation of protein hydration properties from static structures.
Article in Communications chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Deep GIST: Deep Learning Models for Predicting the Distribution of Hydration Thermodynamics around Proteins.Journal of chemical information and modeling · 2026Article
- Superwater as a generative AI framework to predict water molecule positions on protein structures.Communications chemistry · 2025Article
- SuperMetal: a generative AI framework for rapid and precise metal ion location prediction in proteins.Journal of cheminformatics · 2025Article
- Accelerated Hydration Site Localization and Thermodynamic Profiling.Journal of chemical information and modeling · 2025Article
- Improvement in positional accuracy of neural-network predicted hydration sites of proteins by incorporating atomic details of water-protein interactions and site-searching algorithm.Biophysics and physicobiology · 2025Article
- HydraProt: A New Deep Learning Tool for Fast and Accurate Prediction of Water Molecule Positions for Protein Structures.Journal of chemical information and modeling · 2024Article
- WaterKit: Thermodynamic Profiling of Protein Hydration Sites.Journal of chemical theory and computation · 2023Article
- Prediction of hydrophilic and hydrophobic hydration structure of protein by neural network optimized using experimental data.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Complex molecular simulation methods are typically required to calculate the thermodynamic properties of biochemical systems. One example thereof is the thermodynamic profiling of (de)solvation of proteins, which is an essential driving force for protein-ligand and protein-protein binding. The thermodynamic state of water molecules depends on its enthalpic and entropic components; the latter is governed by dynamic properties of the molecule. Here, we developed, to the best of our knowledge, two novel machine learning methods based on deep neural networks that are able to generate the converged thermodynamic state of dynamic water molecules in the heterogeneous protein environment based solely on the information of the static protein structure. The applicability of our machine learning methods to predict the hydration information is demonstrated in two different studies, the qualitative analysis and quantitative prediction of structure-activity relationships, and the prediction of protein-ligand binding modes.
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Registered trials
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