Virtual screening of GPCRs: an in silico chemogenomics approach

TL;DR

This paper introduces new in silico chemogenomics methods based on support vector machines to predict GPCR-ligand interactions, addressing structural and data limitations in drug discovery.

Contribution

It extends existing machine learning strategies by integrating diverse descriptors and biological information, improving GPCR ligand prediction accuracy, especially for orphan receptors.

Findings

Achieved 78.1% accuracy in predicting ligands for orphan GPCRs.

Incorporating hierarchical classification and key residues enhances prediction performance.

Using 2D and 3D descriptors improves model robustness.

Abstract

The G-protein coupled receptor (GPCR) superfamily is currently the largest class of therapeutic targets. \textit{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%.