ArticleJournal of cheminformatics2023
Prediction of organic compound aqueous solubility using machine learning: a comparison study of descriptor-based and fingerprints-based models.
Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Predicting the Aqueous Solubility of Neutral Organic Compounds by Reversed-Phase Liquid Chromatography.Journal of separation science · 2026Article
- Prediction of intrinsic solubility for drug-like organic compounds using automated network optimizer (ANO) for physicochemical feature and hyperparameter optimization.Journal of cheminformatics · 2026Article
- When Does Machine Learning Add Value over Theory? Predicting API Solubility in Binary Mixtures with COSMO-RS and DOOIT2 Across Diverse and Homogeneous Systems.Molecules (Basel, Switzerland) · 2026Article
- Generative AI-Driven Discovery of Next-Generation Electrolytes for Alkali Metal Batteries.Journal of chemical information and modeling · 2026Article
- A High-Performance and Interpretable pMolecules (Basel, Switzerland) · 2026Article
- Data curation in cheminformatics: importance and implementation.Journal of cheminformatics · 2026Review
- Predicting SJournal of chemical theory and computation · 2026Article
- Article
- Rethinking the AI Paradigm for Solubility Prediction of Drug‑Like Compounds with Dual-Perspective Modeling and Experimental Validation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Data-Driven Classification of Solubility Space in Deep Eutectic Solvents: Deciphering Driving Forces Using PCA and K-Means Clustering.Molecules (Basel, Switzerland) · 2025Article
- Physics-Based Solubility Prediction for Organic Molecules.Chemical reviews · 2025Review
- Machine learning analysis of molecular dynamics properties influencing drug solubility.Scientific reports · 2025Article
- A unified approach to inferring chemical compounds with the desired aqueous solubility.Journal of cheminformatics · 2025Article
- A data-driven generative strategy to avoid reward hacking in multi-objective molecular design.Nature communications · 2025Article
- Thermodynamic Assessment of the Pyrazinamide Dissolution Process in Some Organic Solvents.Molecules (Basel, Switzerland) · 2024Article
- Correction: Predicting small molecules solubility on endpoint devices using deep ensemble neural networks.Digital discovery · 2024Article
- Predicting small molecules solubility on endpoint devices using deep ensemble neural networks.Digital discovery · 2024Article
- A Universal Framework for General Prediction of Physicochemical Properties: The Natural Growth Model.Research (Washington, D.C.) · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
A reliable and practical determination of a chemical species' solubility in water continues to be examined using empirical observations and exhaustive experimental studies alone. Predictions of chemical solubility in water using data-driven algorithms can allow us to create a rationally designed, efficient, and cost-effective tool for next-generation materials and chemical formulations. We present results from two machine learning (ML) modeling studies to adequately predict various species' solubility using data for over 8400 compounds. Molecular-descriptors, the most used method in previous studies, and Morgan fingerprint, a circular-based hash of the molecules' structures, were applied to produce water solubility estimates. We trained all models on 80% of the total datasets using the Random Forest (RFs) technique as the regressor and tested the prediction performance using the remaining 20%, resulting in coefficient of determination (R
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