Computer-aided pattern scoring (C@PS): a novel cheminformatic workflow to predict ligands with rare modes-of-action

Autor: Sven Marcel Stefan, Katja Stefan, Vigneshwaran Namasivayam
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Cheminformatics, Vol 16, Iss 1, Pp 1-14 (2024)
Druh dokumentu: article
ISSN: 1758-2946
01612964
DOI: 10.1186/s13321-024-00901-5
Popis: Abstract The identification, establishment, and exploration of potential pharmacological drug targets are major steps of the drug development pipeline. Target validation requires diverse chemical tools that come with a spectrum of functionality, e.g., inhibitors, activators, and other modulators. Particularly tools with rare modes-of-action allow for a proper kinetic and functional characterization of the targets-of-interest (e.g., channels, enzymes, receptors, or transporters). Despite, functional innovation is a prime criterion for patentability and commercial exploitation, which may lead to therapeutic benefit. Unfortunately, data on new, and thus, undruggable or barely druggable targets are scarce and mostly available for mainstream modes-of-action only (e.g., inhibition). Here we present a novel cheminformatic workflow—computer-aided pattern scoring (C@PS)—which was specifically designed to project its prediction capabilities into an uncharted domain of applicability.
Databáze: Directory of Open Access Journals
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