In silico systems for predicting chemical-induced side effects using known and potential chemical protein interactions, enabling mechanism estimation.

In silico models for predicting chemical-induced side effects have become increasingly important for the development of pharmaceuticals and functional food products. However, existing predictive models have difficulty in estimating the mechanisms of side effects in terms of molecular targets or they do not cover the wide range of pharmacological targets. In the present study, we constructed novel in silico models to predict chemical-induced side effects and estimate the underlying mechanisms with high general versatility by integrating the comprehensive prediction of potential chemical-protein interactions (CPIs) with machine learning. First, the potential CPIs were comprehensively estimated by chemometrics based on the known CPI data (1,179,848 interactions involving 3,905 proteins and 824,143 chemicals). Second, the predictive models for 61 side effects in the cardiovascular system (CVS), gastrointestinal system (GIS), and central nervous system (CNS) were constructed by sparsity-induced classifiers based on the known and potential CPI data. The cross validation experiments showed that the proposed CPI-based models had a higher or comparable performance than the traditional chemical structure-based models. Moreover, our enrichment analysis indicated that the highly weighted proteins derived from predictive models could be involved in the corresponding functions of the side effects. For example, in CVS, the carcinogenesis-related pathways (e.g., prostate cancer, PI3K-Akt signal pathway), which were recently reported to be involved in cardiovascular side effects, were enriched. Therefore, our predictive models are biologically valid and would be useful for predicting side effects and novel potential underlying mechanisms of chemical-induced side effects.