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In silico prediction of drug-induced developmental toxicity by using machine learning approaches

  • Hui ZhangEmail author
  • Jun Mao
  • Hua-Zhao Qi
  • Lan DingEmail author
Original Article
  • 23 Downloads

Abstract

Some drugs and xenobiotics have the potential to disturb homeostasis, normal growth, differentiation, development or behavior during prenatal development or postnatally until puberty. Assessment of the developmental toxicity is one of the important safety considerations incorporated by international regulatory agencies. In this investigation, seven machine learning methods, including naïve Bayes, support vector machine, recursive partitioning, k-nearest neighbor, C4.5 decision tree, random forest and Adaboost, were used to build binary classification models for developmental toxicity. Among these models, the naïve Bayes classifier represented the best predictive performance and stability, which gave 91.11% overall prediction accuracy, 91.50% balanced accuracy and 0.818 MCC for the training set, and generated 83.93% concordance, 81.85% balanced accuracy and 0.627 MCC for the test set. The application domains were analyzed, and only one chemical in the test set was identified as outside the application domain. In addition, 10 important molecular descriptors related to developmental toxicity were selected by the genetic algorithm, which may contribute to explanation of the mechanisms of developmental toxicants. The best naïve Bayes classification model should be employed as alternative method for qualitative prediction of chemical-induced developmental toxicity in early stages of drug development.

Graphic abstract

Keywords

Developmental toxicity Machine learning In silico prediction Molecular descriptor Genetic algorithm 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant nos. 81660589 and 81903543).

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

11030_2019_9991_MOESM1_ESM.xlsx (23 kb)
Supplementary material 1 (XLSX 22 kb)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Life ScienceNorthwest Normal UniversityLanzhouPeople’s Republic of China
  2. 2.State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical SchoolSichuan UniversityChengduPeople’s Republic of China

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