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.

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Combinatorial Chemistry and High Throughput Screening
Department of Medical Informatics

Liu, T. (Tao), Chen, L. (Lei), & Pan, X. (2018). An integrated multi-label classifier with chemical-chemical interactions for prediction of chemical toxicity effects. Combinatorial Chemistry and High Throughput Screening, 21(6), 403–410. doi:10.2174/1386207321666180601075428