Prediction of organic compound aqueous solubility using machine learning: a comparison study of descriptor-based and fingerprints-based models.

ABSTRACT: A reliable and practical determination of a chemical species' solubility in water continues to be examined using empirical observations and exhaustive experimental studies alone. Predictions of chemical solubility in water using data-driven algorithms can allow us to create a rationally designed, efficient, and cost-effective tool for next-generation materials and chemical formulations. We present results from two machine learning (ML) modeling studies to adequately predict various species' solubility using data for over 8400 compounds. Molecular-descriptors, the most used method in previous studies, and Morgan fingerprint, a circular-based hash of the molecules' structures, were applied to produce water solubility estimates. We trained all models on 80% of the total datasets using the Ran

SUBMITTER: Tayyebi A 

PROVIDER: S-EPMC10583449 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature