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The G-protein coupled receptor (GPCR) superfamily is currently the largestclass of therapeutic targets. \textit{In silico} prediction of interactionsbetween GPCRs and small molecules is therefore a crucial step in the drugdiscovery process, which remains a daunting task due to the difficulty tocharacterize the 3D structure of most GPCRs, and to the limited amount of knownligands for some members of the superfamily. Chemogenomics, which attempts tocharacterize interactions between all members of a target class and all smallmolecules simultaneously, has recently been proposed as an interestingalternative to traditional docking or ligand-based virtual screeningstrategies. We propose new methods for in silico chemogenomics and validatethem on the virtual screening of GPCRs. The methods represent an extension of arecently proposed machine learning strategy, based on support vector machines(SVM), which provides a flexible framework to incorporate various informationsources on the biological space of targets and on the chemical space of smallmolecules. We investigate the use of 2D and 3D descriptors for small molecules,and test a variety of descriptors for GPCRs. We show fo instance thatincorporating information about the known hierarchical classification of thetarget family and about key residues in their inferred binding pocketssignificantly improves the prediction accuracy of our model. In particular weare able to predict ligands of orphan GPCRs with an estimated accuracy of78.1%.

Virtual screening of GPCRs: an in silico chemogenomics approach