Drug Toxicity Prediction by Machine Learning Approaches

Yucong Shen, Frank Y. Shih, Hao Chen

Research output: Contribution to journalArticlepeer-review


Drug property prediction, especially toxicity, helps reduce risks in a range of real-world applications. In this paper, we aim to apply various machine-learning models for solving the drug toxicity prediction problem. Among various machine-learning approaches, we select five suitable representatives: random forest, multi-layer perceptron, logistic regression, graph convolutional neural network, and graph isomorphism network (GIN) for conducting experiments on six datasets for toxicity prediction, including Tox 21, ClinTox, ToxCast, SIDER, HIV, and BACE. We design the GIN with four hidden layers and select the Adam optimizer with the learning rate 10-4 and the batch size 256. Furthermore, we use a batch norm layer inside each of the GIN hidden layers. Experimental results show that the designed GIN model is most efficient in distinguishing between safe and toxic drugs and outperforms the others under the supervision of ROC AUC score and recall.

Original languageEnglish (US)
Article number2351013
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number10
StatePublished - Aug 1 2023

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


  • Deep learning
  • graph neural networks
  • toxicity prediction


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