期刊
出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/10590501.2018.1537118
关键词
Toxicity prediction; machine learning; structure-activity relationship; prediction accuracy; model reliability; molecular descriptors
资金
- U.S. Army Environmental Quality and Installations Program
In silico toxicity prediction plays an important role in the regulatory decision making and selection of leads in drug design as in vitro/vivo methods are often limited by ethics, time, budget, and other resources. Many computational methods have been employed in predicting the toxicity profile of chemicals. This review provides a detailed end-to-end overview of the application of machine learning algorithms to Structure-Activity Relationship (SAR)-based predictive toxicology. From raw data to model validation, the importance of data quality is stressed as it greatly affects the predictive power of derived models. Commonly overlooked challenges such as data imbalance, activity cliff, model evaluation, and definition of applicability domain are highlighted, and plausible solutions for alleviating these challenges are discussed.
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