ArticleJournal of cheminformatics2025
Prediction of the water solubility by a graph convolutional-based neural network on a highly curated dataset.
Article in Journal of cheminformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Prediction of Solute Descriptors for Linear Solvation Energy Relationships Using K‑Nearest Neighbors, Group Contributions, and Graph-Convolutional Neural Networks.ACS environmental Au · 2026Article
- MolProphecy: Bridging medicinal chemists' knowledge and molecular pre-trained models via a multi-modal framework.Journal of advanced research · 2026Article
- Data-Driven Classification of Solubility Space in Deep Eutectic Solvents: Deciphering Driving Forces Using PCA and K-Means Clustering.Molecules (Basel, Switzerland) · 2025Article
- Duality of Simplicity and Accuracy in QSPR: A Machine Learning Framework for Predicting Solubility of Selected Pharmaceutical Acids in Deep Eutectic Solvents.Molecules (Basel, Switzerland) · 2025Article
- Systematic benchmarking of 13 AI methods for predicting cyclic peptide membrane permeability.Journal of cheminformatics · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Water solubility is a relevant physico-chemcial property in environmental chemistry, toxicology, and drug design. Although the water solubility is besides the octanol-water partition coefficient, melting point, and boiling point a property with a large amount of available experimental data, there are still more compounds in the chemical universe for which information on their water solubility is lacking. Thus, prediction tools with a broad application domain are needed to fill the corresponding data gaps. To this end, we developed a graph convolutional neural network model (GNN) to predict the water solubility in the form of log S
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Registered trials
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