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Motivation The identification of protein function and the prediction of ligand-target interaction is an active research field that is facilitated by means of categorizing ligands and proteins into biologically sensible groups. Because of the pharmacological fact that related drugs can bind to receptors without obvious sequence or structural similarity, it is appropriate to categorize proteins based not only on their sequence or structures but also on the chemical structure and the phenotypic side-effect of their ligands. In chemogenomic studies where the complete set of ligands for a protein is not known a priori, integrating the de novo detection of interacting ligand and protein groups into the categorization process can guide the process towards more biologically sensible solutions.

Integrative biclustering of heterogeneous datasets using a Bayesian nonparametric model with application to chemogenomics