In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning

• Published in: Journal of chemical information and modeling Vol. 57; no. 1; pp. 36 - 49
• Main Authors: Zang, Qingda, Mansouri, Kamel, Williams, Antony J, Judson, Richard S, Allen, David G, Casey, Warren M, Kleinstreuer, Nicole C
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 23.01.2017
• Subjects: Artificial intelligence; Chemical Phenomena; Chemicals; Chemistry; Computer Simulation; Environmental Pollutants - chemistry; Environmental Pollutants - toxicity; Informatics; Machine Learning; Molecules; Quantitative Structure-Activity Relationship; Solubility; Transition Temperature; Vapor Pressure; Water - chemistry

There are little available toxicity data on the vast majority of chemicals in commerce. High-throughput screening (HTS) studies, such as those being carried out by the U.S. Environmental Protection Agency (EPA) ToxCast program in partnership with the federal Tox21 research program, can generate biol...