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Related Experiment Video

Updated: Jun 16, 2026

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English
14:34

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

Published on: April 3, 2026

iPHACE: integrative navigation in pharmacological space.

Ricard Garcia-Serna1, Oleg Ursu, Tudor I Oprea

  • 1Chemogenomics Laboratory, Research Unit on Biomedical Informatics (GRIB), Institut Municipal d'Investigació Mèdica and Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain.

Bioinformatics (Oxford, England)
|February 17, 2010
PubMed
Summary
This summary is machine-generated.

A new web tool, iPHACE, helps researchers explore drug-protein interactions. It integrates chemical and biological data, enabling better understanding of drug profiles and target interactions.

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Last Updated: Jun 16, 2026

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English
14:34

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

Published on: April 3, 2026

Area of Science:

  • Pharmacology
  • Cheminformatics
  • Bioinformatics

Background:

  • The increasing volume of experimentally determined drug binding affinities necessitates advanced data mining and visualization tools.
  • Existing tools often lack the ability to integrate diverse chemical and biological data effectively.

Purpose of the Study:

  • To develop an integrative web-based tool, iPHACE, for navigating the pharmacological space.
  • To facilitate the extraction of knowledge from drug target profiles and protein target profiles.

Main Methods:

  • Development of iPHACE, an integrative web-based platform.
  • Integration of data from IUPHAR-DB (small molecule drugs) and PDSP (drug-protein interactions).
  • Implementation of advanced querying and filtering functionalities beyond traditional methods.

Main Results:

  • iPHACE provides a graphical environment for visualizing drug-protein interactions.
  • The tool enables exploration of the pharmacological space defined by small molecule drugs.
  • Knowledge extraction from both drug-centric and target-centric perspectives is facilitated.

Conclusions:

  • iPHACE addresses the need for specialized tools to handle large-scale drug-target interaction data.
  • The platform enhances the understanding of pharmacological relationships by integrating chemical and biological information.
  • iPHACE offers a novel approach to mining and visualizing complex drug-target data.