Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints

Huawei Feng, Li Zhang, Shimeng Li, Lili Liu, Tianzhou Yang, Pengyu Yang, Jian Zhao, Isaiah Tuvia Arkin, Hongsheng Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

Reproductive toxicity endpoints are a significant safety concern in the assessment of the adverse effects of chemicals in drug discovery. Computational models that can accurately predict a chemical's toxic potential are increasingly pursued to replace traditional animal experiments. Thus, ensemble learning models were built to predict the reproductive toxicity of compounds. Our ensemble models were developed using support vector machine, random forest, and extreme gradient boosting methods and 9 molecular fingerprints calculated for a dataset containing 1823 chemicals. The best prediction performance was achieved by the Ensemble-Top12 model, with an accuracy (ACC) of 86.33 %, a sensitivity (SEN) of 82.02 %, a specificity (SPE) of 90.19 %, and an area under the receiver operating characteristic curve (AUC) of 0.937 in 5-fold cross-validation and ACC, SEN, SPE, and AUC values of 84.38 %, 86.90 %, 90.67 %, and 0.920, respectively, in external validation. We also defined the applicability domain (AD) of the ensemble model by calculating the Tanimoto distance of the training set. Compared with models in existing literature, our ensemble model achieves relatively high ACC, SPE and AUC values. We also identified several fingerprint features related to chemical reproductive toxicity. Considering the performance of model, we recommend using the Ensemble-Top12 model to predict reproductive toxicity in early drug development.

Original languageAmerican English
Pages (from-to)4-14
Number of pages11
JournalToxicology Letters
Volume340
DOIs
StatePublished - 1 Apr 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier B.V.

Keywords

  • Ensemble
  • Machine learning
  • Molecular fingerprint
  • Prediction models
  • Reproductive toxicity

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