ArticleScientific data2025
BigSolDB 2.0, dataset of solubility values for organic compounds in different solvents at various temperatures.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- An accessible property classification framework to predict the solubility of functionalised naphthalenes and rylenes in organic solvents.Digital discovery · 2026Article
- Data-Driven Critical Evaluation of the General Solubility Equation.Journal of chemical information and modeling · 2026Article
- 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
- BigSolDB 2.0, dataset of solubility values for organic compounds in different solvents at various temperatures.Scientific data · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Solubility is one of the key properties of organic compounds that determines their applications in chemistry, materials science and pharmaceuticals. However, predicting solubility values in any solvent except water from a molecular structure still remains a challenging task in modern cheminformatics, not least due to the lack of large and diverse datasets. In this study, we present a dataset containing 103944 experimental solubility values within a temperature range from 243 to 425 K for 1448 organic compounds measured in 213 individual solvents extracted from 1595 peer-reviewed articles. The molecular structures of solutes and solvents as well as solubility data are standardized and provided in a machine-readable format, allowing straightforward data-driven analysis. We have also developed a web-tool for interactive visualization and search within the dataset. This dataset can serve as a comprehensive benchmark for developing machine learning for predicting solubility.
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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.