Drug design methodologies based on fused data could be the next generation of tools used in rational design, especially to decode complex pharmacological issues like DILI events. Here, we introduce a combined analysis of DILI-related events using the concept of consensus chemical space and the chemical multiverse, using chemical, physicochemical, structural, biochemical, and biological data to improve the understanding of DILI events. Our results, which suggest that the combination of chemical structural and biological data improves the clustering of associated DILI compounds, pave the way to new opportunities to develop predictive models (like machine and deep learning models) capable of predicting DILI events in an early stage of the drug development process. It was also concluded that organohalogens, phenylpropanoids, polyketides, organic acids, organosulfur, alkaloids, and organophosphorus compounds are associated with a higher rate of DILI events. For this reason, we suggest more exhaustive preliminary studies for these types of compounds with the aim of reducing the cases associated with DILI events.