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Kernel methods for in silico chemogenomics

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.
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Preprints, Working Papers, ...
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Contributor : Laurent Jacob Connect in order to contact the contributor
Submitted on : Tuesday, September 25, 2007 - 1:50:37 PM
Last modification on : Wednesday, November 17, 2021 - 12:30:56 PM
Long-term archiving on: : Thursday, April 8, 2010 - 8:23:40 PM


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  • HAL Id : hal-00174808, version 1
  • ARXIV : 0709.3931


Laurent Jacob, Jean-Philippe Vert. Kernel methods for in silico chemogenomics. 2007. ⟨hal-00174808⟩



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