A Review On Machine Learning Methods For In Silico Toxicity Prediction

Authors

Gabriel Idakwo, University of Southern Mississippi
Joseph Luttrell IV, University of Southern MississippiFollow
Minjun Chen, US Food and Drug AdministrationFollow
Huixiao Hong, US Food and Drug AdministrationFollow
Zhaoxian Zhou, University of Southern MississippiFollow
Ping Gong, US Army Engineer Research and Development CenterFollow
Chaoyang Zhang, University of Southern MississippiFollow

Document Type

Article

Publication Date

1-10-2019

Department

Computing

School

Computing Sciences and Computer Engineering

Abstract

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.

Publication Title

Journal of Environmental Science and Health, Part C

Volume

36

Issue

4

First Page

169

Last Page

191

Recommended Citation

Idakwo, G., Luttrell, J., Chen, M., Hong, H., Zhou, Z., Gong, P., Zhang, C. (2019). A Review On Machine Learning Methods For In Silico Toxicity Prediction. Journal of Environmental Science and Health, Part C, 36(4), 169-191.
Available at: https://aquila.usm.edu/fac_pubs/15839