Aims and Objective: Chemical toxicity effect is one of the major reasons for declining
candidate drugs. Detecting the toxicity effects of all chemicals can accelerate the procedures of drug
discovery. However, it is time-consuming and expensive to identify the toxicity effects of a given
chemical through traditional experiments. Designing quick, reliable and non-animal-involved
computational methods is an alternative way.
Method: In this study, a novel integrated multi-label classifier was proposed. First, based on five types
of chemical-chemical interactions retrieved from STITCH, each of which is derived from one aspect of
chemicals, five individual classifiers were built. Then, several integrated classifiers were built by
integrating some or all individual classifiers.
Result and Conclusion: By testing the integrated classifiers on a dataset with chemicals and their
toxicity effects in Accelrys Toxicity database and non-toxic chemicals with their performance evaluated
by jackknife test, an optimal integrated classifier was selected as the proposed classifier, which
provided quite high prediction accuracies and wide applications.