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.
Highlights
Introduction to methodology and encoding rulesJournal of Chemical Information and Computer Sciences 28, 31–36 (1988). 12
It is of critical importance in especially pharmaceutical drug design, where poor aqueous solubility is likely to lead to precipitation of compounds from screening buffer, which may create a high risk of erroneous results, false leads, and increased costs and formulation difficulties during clinical development
We provided a general algorithm for selection of the statistically most reliable values from a set of competing values
Summary
Aqueous solubility constitutes a crucial property of chemical substances that governs behavior of phenomena in several areas like geochemistry, climate predictions, biochemistry, drug-design, agrochemical design, and protein ligand binding It is defined as the maximum amount of a compound, i.e., the solute, that can get dissolved in a given volume of water, and depends on physical conditions such as temperature and pressure. Machine learning models developed using datasets, which have small size and lack chemical diversity, show poor predictive capability on external test sets, as shown in the study by Wang et al.8 AqSolDB is an openly accessible, easy-to-use, and well-structured database of compound We expect it to serve a broad community as a reference aqueous solubility dataset for the bench-marking of new experimental and physics-based modelling results, and as machine-readable ancillary resource to improve the prediction capability of future machine learning approaches
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