ArticleScientific data2022
Boosting the predictive performance with aqueous solubility dataset curation.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Deciphering Molecular and Solvent Effects on Aqueous and Organic Solubility through Interpretable Machine Learning Approaches.ACS omega · 2026Article
- Dataset of solubility values for organic compounds in binary mixtures of solvents at various temperatures.Scientific data · 2026Article
- Data curation in cheminformatics: importance and implementation.Journal of cheminformatics · 2026Review
- Advanced machine learning for innovative drug discovery.Journal of cheminformatics · 2025Article
- BigSolDB 2.0, dataset of solubility values for organic compounds in different solvents at various temperatures.Scientific data · 2025Article
- A unified approach to inferring chemical compounds with the desired aqueous solubility.Journal of cheminformatics · 2025Article
- Be aware of overfitting by hyperparameter optimization!Journal of cheminformatics · 2024Article
- PharmaBench: Enhancing ADMET benchmarks with large language models.Scientific data · 2024Article
- Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility.ACS omega · 2024Article
- Will we ever be able to accurately predict solubility?Scientific data · 2024Article
- Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods.Journal of chemical information and modeling · 2023Review
- Artificial Intelligence-Based Quantitative Structure-Property Relationship Model for Predicting Human Intestinal Absorption of Compounds with Serotonergic Activity.Molecular pharmaceutics · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intrinsic solubility is a critical property in pharmaceutical industry that impacts in-vivo bioavailability of small molecule drugs. However, solubility prediction with Artificial Intelligence(AI) are facing insufficient data, poor data quality, and no unified measurements for AI and physics-based approaches. We collect 7 aqueous solubility datasets, and present a dataset curation workflow. Evaluating the curated data with two expanded deep learning methods, improved RMSE scores on all curated thermodynamic datasets are observed. We also compare expanded Chemprop enhanced with curated data and state-of-art physics-based approach using pearson and spearman correlation coefficients. A similar performance on pearson with 0.930 and spearman with 0.947 from expanded Chemprop is achieved. A steadily improved pearson and spearman values with increasing data points are also illustrated. Besides that, the computation advantage of AI models enables quick evaluation of a large set of molecules during the hit identification or lead optimization stages, which helps further decision making within the time cycle at drug discovery stage.
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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.