Direct Prediction of Physicochemical Properties and Toxicities of Chemicals from Analytical Descriptors by GC-MS.

ABSTRACT: With advances in machine learning (ML) techniques, the quantitative structure-activity relationship (QSAR) approach is becoming popular for evaluating chemicals. However, the QSAR approach requires that the chemical structure of the target compound is known and that it should be convertible to molecular descriptors. These requirements lead to limitations in predicting the properties and toxicities of chemicals distributed in the environment as in the PubChem database; the structural information on only 14% of compounds is available. This study proposes a new ML-based QSAR approach that can predict the properties and toxicities of compounds using analytical descriptors of mass spectrum and retention index obtained via gas chromatography-mass spectrometry without requiring exact structural i

SUBMITTER: Zushi Y 

PROVIDER: S-EPMC9246259 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature