Prediction of the water solubility by a graph convolutional-based neural network on a highly curated dataset.

ABSTRACT: Water solubility is a relevant physico-chemcial property in environmental chemistry, toxicology, and drug design. Although the water solubility is besides the octanol-water partition coefficient, melting point, and boiling point a property with a large amount of available experimental data, there are still more compounds in the chemical universe for which information on their water solubility is lacking. Thus, prediction tools with a broad application domain are needed to fill the corresponding data gaps. To this end, we developed a graph convolutional neural network model (GNN) to predict the water solubility in the form of log S_w based on a highly curated dataset of 9800 chemicals. We started our model development with a curation workflow of the AqSolDB data, ending with 7605

SUBMITTER: Ulrich N 

PROVIDER: S-EPMC12012962 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature