Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility.

ABSTRACT: The solubility of chemical substances in water is a critical parameter in pharmaceutical development, environmental chemistry, agrochemistry, and other fields; however, accurately predicting it remains a challenge. This study aims to evaluate and compare the effectiveness of some of the most popular machine learning modeling methods and molecular featurization techniques in predicting aqueous solubility. Although these methods were not implemented in a competitive environment, some of their performance surpassed previous benchmarks, offering gradual but significant improvements. Our results show that methods based on graph convolution and graph attention mechanisms demonstrated exceptional predictive abilities with high-quality data sets, albeit with a sensitivity to data noise and errors.

SUBMITTER: Zheng T 

PROVIDER: S-EPMC11325511 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature