ArticleScientific data2019
AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds.
Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
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Who cites it
78 citing papers in PubMed.
- Conformer-covariance diagnostics for aqueous solubility.Journal of computer-aided molecular design · 2026Article
- Data-Driven Critical Evaluation of the General Solubility Equation.Journal of chemical information and modeling · 2026Article
- Can Graph Neural Networks Understand Chemical Elements?Journal of chemical information and modeling · 2026Article
- The limits of bio-molecular modeling with large language models: a cross-scale evaluation.Bioinformatics (Oxford, England) · 2026Article
- Predicting the Aqueous Solubility of Neutral Organic Compounds by Reversed-Phase Liquid Chromatography.Journal of separation science · 2026Article
- Automatic identification of compounds in molecular mixtures from liquid-phase infrared spectra.Chemical science · 2026Article
- Oral bioavailability property prediction based on task similarity transfer learning.Molecular diversity · 2026Article
- Deciphering Molecular and Solvent Effects on Aqueous and Organic Solubility through Interpretable Machine Learning Approaches.ACS omega · 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
- Physics-informed deep learning for molecular solubility prediction: integrating thermodynamic constraints with neural network architectures.Scientific reports · 2026Article
- Dataset of solubility values for organic compounds in binary mixtures of solvents at various temperatures.Scientific data · 2026Article
- New Quinazolin-4(3Molecules (Basel, Switzerland) · 2025Article
- 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
- Predicting drug solubility in binary solvent mixtures using graph convolutional networks: a comprehensive deep learning approach.Scientific reports · 2025Article
- A fragment based approach towards curating, comparing and developing machine learning models applied in photochemistry.Chemical science · 2025Article
- Data-Driven Classification of Solubility Space in Deep Eutectic Solvents: Deciphering Driving Forces Using PCA and K-Means Clustering.Molecules (Basel, Switzerland) · 2025Article
- Update and ADMET Profile of the Latin American Natural Product Database: LANaPDB.Molecular informatics · 2025Article
- Artificial intelligence modelling of tyrosine kinase inhibitors at risk of malabsorption and bioavailability-enhancing strategies.British journal of clinical pharmacology · 2025Article
- Enhancing molecular property prediction through data integration and consistency assessment.Journal of cheminformatics · 2025Article
- MolAI: A Deep Learning Framework for Data-Driven Molecular Descriptor Generation and Advanced Drug Discovery Applications.Journal of chemical information and modeling · 2025Article
18 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Water is a ubiquitous solvent in chemistry and life. It is therefore no surprise that the aqueous solubility of compounds has a key role in various domains, including but not limited to drug discovery, paint, coating, and battery materials design. Measurement and prediction of aqueous solubility is a complex and prevailing challenge in chemistry. For the latter, different data-driven prediction models have recently been developed to augment the physics-based modeling approaches. To construct accurate data-driven estimation models, it is essential that the underlying experimental calibration data used by these models is of high fidelity and quality. Existing solubility datasets show variance in the chemical space of compounds covered, measurement methods, experimental conditions, but also in the non-standard representations, size, and accessibility of data. To address this problem, we generated a new database of compounds, AqSolDB, by merging a total of nine different aqueous solubility datasets, curating the merged data, standardizing and validating the compound representation formats, marking with reliability labels, and providing 2D descriptors of compounds as a Supplementary Resource.
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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.