Sažetak We describe three machine learning models submitted to the 2019 Solubility
Challenge. All are founded on tree-like classifiers, with one model being based on Random …

Accurate prediction of thermodynamic solubility by machine learning remains a challenge.
Recent models often display good performances, but their reliability may be deceiving when …

Solubility is a key metric for therapeutic compounds. Conversely, insoluble compounds
cloud the accuracy of assays at all stages of chemical biology and drug discovery. Herein …

Accurate methods to predict solubility from molecular structure are highly sought after in the
chemical sciences. To assess the state of the art, the American Chemical Society organized …

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 …

Sažetak Yalkowsky's General Solubility Equation (GSE), with its three fixed constants, is
popular and easy to apply, but is not very accurate for polar, zwitterionic, or flexible …

Ten years ago, we issued an open prediction challenge to the cheminformatics community:
would participants be able to predict the equilibrium intrinsic solubilities of 32 druglike …

While accurate prediction of aqueous solubility remains a challenge in drug discovery,
machine learning (ML) approaches have become increasingly popular for this task. For …

The EUOS/SLAS challenge aimed to facilitate the development of reliable algorithms to
predict the aqueous solubility of small molecules using experimental data from 100K …

We report a single machine learning (ML)-based model to predict the solubility of drug/drug-
like compounds across 49 organic solvents, extensible to more. By adopting a cross-solvent …