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Mixed learning algorithms and features ensemble in hepatotoxicity prediction

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Abstract

Drug-induced liver injury, although infrequent, is an important safety concern that can lead to fatality in patients and failure in drug developments. In this study, we have used an ensemble of mixed learning algorithms and mixed features for the development of a model to predict hepatic effects. This robust method is based on the premise that no single learning algorithm is optimum for all modelling problems. An ensemble model of 617 base classifiers was built from a diverse set of 1,087 compounds. The ensemble model was validated internally with five-fold cross-validation and 25 rounds of y-randomization. In the external validation of 120 compounds, the ensemble model had achieved an accuracy of 75.0%, sensitivity of 81.9% and specificity of 64.6%. The model was also able to identify 22 of 23 withdrawn drugs or drugs with black box warning against hepatotoxicity. Dronedarone which is associated with severe liver injuries, announced in a recent FDA drug safety communication, was predicted as hepatotoxic by the ensemble model. It was found that the ensemble model was capable of classifying positive compounds (with hepatic effects) well, but less so on negatives compounds when they were structurally similar. The ensemble model built in this study is made available for public use.

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Acknowledgments

This study was supported by the NUS start-up grant R-148-000-105-133.

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Correspondence to Chun Wei Yap.

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Liew, C.Y., Lim, Y.C. & Yap, C.W. Mixed learning algorithms and features ensemble in hepatotoxicity prediction. J Comput Aided Mol Des 25, 855–871 (2011). https://doi.org/10.1007/s10822-011-9468-3

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