Kernel methods for in silico chemogenomics

TL;DR

This paper introduces a kernel-based framework for chemogenomics that improves the prediction of small molecule-protein interactions by sharing information across targets, enhancing drug discovery processes.

Contribution

It presents a systematic kernel method framework for chemogenomics that enables cross-target information sharing, significantly improving ligand prediction accuracy.

Findings

Enhanced prediction accuracy for enzymes, GPCRs, and ion channels.

Effective information sharing across multiple targets.

Potential to accelerate drug discovery and reduce costs.

Abstract

Predicting interactions between small molecules and proteins is a crucial ingredient of the drug discovery process. In particular, accurate predictive models are increasingly used to preselect potential lead compounds from large molecule databases, or to screen for side-effects. While classical in silico approaches focus on predicting interactions with a given specific target, new chemogenomics approaches adopt cross-target views. Building on recent developments in the use of kernel methods in bio- and chemoinformatics, we present a systematic framework to screen the chemical space of small molecules for interaction with the biological space of proteins. We show that this framework allows information sharing across the targets, resulting in a dramatic improvement of ligand prediction accuracy for three important classes of drug targets: enzymes, GPCR and ion channels.