Absorption is an important area of research in pharmacochemistry and drug development,
because the drug has to be absorbed before any drug effects can occur. Furthermore, the …

An outstanding challenge in deep learning in chemistry is its lack of interpretability. The
inability of explaining why a neural network makes a prediction is a major barrier to …

Determining the aqueous solubility of molecules is a vital step in many pharmaceutical,
environmental, and energy storage applications. Despite efforts made over decades, there …

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

We present a collection of publicly available intrinsic aqueous solubility data of 829 drug‐
like compounds. Four different machine learning algorithms (random forests [RF], LightGBM …

Computational methods play a pivotal role in the pursuit of efficient drug discovery, enabling
the rapid assessment of compound properties before costly and time-consuming laboratory …

The solubility of active pharmaceutical ingredients is a mandatory physicochemical
characteristic in pharmaceutical practice. However, the number of potential solvents and …

This study applies the 'Flexible-Acceptor'variant of the General Solubility Equation, GSE (Φ,
B), to the prediction of the aqueous intrinsic solubility, log10 S 0, of FDA recently-approved …

Predicting solubility of small molecules is a very difficult undertaking due to the lack of
reliable and consistent experimental solubility data. It is well known that for a molecule in a …

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 …