Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network.

ABSTRACT: As safety is one of the most important properties of drugs, chemical toxicology prediction has received increasing attentions in the drug discovery research. Traditionally, researchers rely on in vitro and in vivo experiments to test the toxicity of chemical compounds. However, not only are these experiments time consuming and costly, but experiments that involve animal testing are increasingly subject to ethical concerns. While traditional machine learning (ML) methods have been used in the field with some success, the limited availability of annotated toxicity data is the major hurdle for further improving model performance. Inspired by the success of semi-supervised learning (SSL) algorithms, we propose a Graph Convolution Neural Network (GCN) to predict chemical toxicity and trained th

SUBMITTER: Chen J 

PROVIDER: S-EPMC8627024 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature