AI-based QSAR Modeling for Prediction of Active Compounds in MIE/AOP

Author:

Uesawa Yoshihiro1

Affiliation:

1. Department of Medical Molecular Informatics, Meiji Pharmaceutical University

Publisher

Pharmaceutical Society of Japan

Subject

Pharmaceutical Science,Pharmacology

Reference22 articles.

1. 1) Case Studies on the Development and Application of in-Silico Techniques for Environmental Hazard and Risk Assessment (CADASTER), Environmental Toxicity Prediction Challenge: 〈http://www.cadaster.eu/node/65.html〉, cited 26 July, 2019.

2. 2) National Center for Advancing Translational Sciences, National Institutes of Health, Tox21 Data Challenge 2014: 〈https://tripod.nih.gov/tox21/challenge/〉, cited 26 July, 2019.

3. 3) Hansch C., Fujita T., J. Am. Chem. Soc., 86, 1616-1626 (1964).

4. 4) Cronin M. T. D., Richarz A. N., Schultz T. W., Regul. Toxicol. Pharmacol., 106, 90-104 (2019).

5. 5) Heo S., Safder U., Yoo C., Environ. Pollut., 253, 29-38 (2019).

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