ArticleiScience2021
Pushing the limits of solubility prediction via quality-oriented data selection.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Physics-informed deep learning for molecular solubility prediction: integrating thermodynamic constraints with neural network architectures.Scientific reports · 2026Article
- A data-driven elucidation of the challenges in designing stable quinone derivatives for aqueous organic redox flow batteries: correlations between thermodynamics of chemical degradation and energy capacity metrics.Science and technology of advanced materials · 2026Article
- A Solvent Selection Framework for Porous Organic Polymers.Journal of chemical information and modeling · 2025Article
- Advancing Aqueous Solubility Prediction: A Machine Learning Approach for Organic Compounds Using a Curated Data Set.Journal of chemical information and modeling · 2025Article
- Physics-Based Solubility Prediction for Organic Molecules.Chemical reviews · 2025Review
- AI-Driven Drug Discovery: A Comprehensive Review.ACS omega · 2025Review
- Predicting Oxidation Potentials with DFT-Driven Machine Learning.Journal of chemical information and modeling · 2025Article
- Prediction of the water solubility by a graph convolutional-based neural network on a highly curated dataset.Journal of cheminformatics · 2025Article
- Be aware of overfitting by hyperparameter optimization!Journal of cheminformatics · 2024Article
- Thermodynamic Assessment of the Pyrazinamide Dissolution Process in Some Organic Solvents.Molecules (Basel, Switzerland) · 2024Article
- Effect of Data Quality and Data Quantity on the Estimation of Intrinsic Solubility: Analysis Based on a Single-Source Data Set.Molecular pharmaceutics · 2024Article
- Predicting small molecules solubility on endpoint devices using deep ensemble neural networks.Digital discovery · 2024Article
- Merging Counter-Propagation and Back-Propagation Algorithms: Overcoming the Limitations of Counter-Propagation Neural Network Models.International journal of molecular sciences · 2024Article
- Designing solvent systems using self-evolving solubility databases and graph neural networks.Chemical science · 2024Article
- Intrinsic Solubility of Ionizable Compounds from pACS omega · 2023Article
- Prediction of organic compound aqueous solubility using machine learning: a comparison study of descriptor-based and fingerprints-based models.Journal of cheminformatics · 2023Article
- SOMAS: a platform for data-driven material discovery in redox flow battery development.Scientific data · 2022Article
- RedDB, a computational database of electroactive molecules for aqueous redox flow batteries.Scientific data · 2022Article
- Protein Design: From the Aspect of Water Solubility and Stability.Chemical reviews · 2022Review
- Machine learning enables interpretable discovery of innovative polymers for gas separation membranes.Science advances · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.
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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.