2008/01/28 by Laurent Jacob, Brice Hoffmann, Jacob, Laurent +6
Biochemistry, Genetics and Molecular Biology · Computer Science · #Chemical Synthesis and Analysis #Computational Drug Discovery Methods #FOS: Biological sciences #Quantitative Methods (q-bio.QM) #Receptor Mechanisms and Signaling #q-bio.QM
paper · pdf · doi:10.48550/arxiv.0801.4301
arxiv created 2008/01/28 · openalex publication_date 2008/01/28 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The G-protein coupled receptor (GPCR) superfamily is currently the largest class of therapeutic targets. In silico prediction of interactions between GPCRs and small molecules is therefore a crucial step in the drug discovery process, which remains a daunting task due to the difficulty to characterize the 3D structure of most GPCRs, and to the limited amount of known ligands for some members of the superfamily. Chemogenomics, which attempts to characterize interactions between all members of a target class and all small molecules simultaneously, has recently been proposed as an interesting alternative to traditional docking or ligand-based virtual screening strategies. We propose new methods for in silico chemogenomics and validate them on the virtual screening of GPCRs. The methods represent an extension of a recently proposed machine learning strategy, based on support vector machines (SVM), which provides a flexible framework to incorporate various information sources on the biological space of targets and on the chemical space of small molecules. We investigate the use of 2D and 3D descriptors for small molecules, and test a variety of descriptors for GPCRs. We show fo instance that incorporating information about the known hierarchical classification of the target family and about key residues in their inferred binding pockets significantly improves the prediction accuracy of our model. In particular we are able to predict ligands of orphan GPCRs with an estimated accuracy of 78.1%.