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

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.

Authors

Institutions

Publication Details

Journal
Journal of Cheminformatics
Published
2024-09-23
DOI
https://doi.org/10.1186/s13321-024-00901-5
Citations
5
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.81

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Sven Marcel Stefan, Vigneshwaran Namasivayam
5 citations
Journal of Cheminformatics
Computational Drug Discovery Methods
1.81
article

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

Sven Marcel Stefan, Vigneshwaran Namasivayam
article en
5 citations

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.

Journal of CheminformaticsVol. 16(1)
Medical University of Lublin (PL), Oslo University Hospital (NO), University of Bonn (DE), University of Lübeck (DE)
Deutsche Forschungsgemeinschaft, Rheinische Friedrich-Wilhelms-Universität Bonn
Industry, innovation and infrastructure
Openalex Percentile: Top 13%
Computational Drug Discovery Methods
1.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Computer-aided pattern scoring (C@PS): a novel cheminformatic workflow to predict ligands with rare modes-of-action — Sven Marcel Stefan, Vigneshwaran Namasivayam · Journal of Cheminformatics (2024) | TGRS Research Map | TGRS