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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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Empowering pharmacoinformatics by linked life science data.

Daria Goldmann1, Barbara Zdrazil1, Daniela Digles1

  • 1Department of Pharmaceutical Chemistry, University of Vienna, Althanstraße 14, 1090, Vienna, Austria.

Journal of Computer-Aided Molecular Design
|November 11, 2016
PubMed
Summary

Large public databases and workflow engines simplify data retrieval for drug discovery projects. This enables advanced analyses, moving beyond traditional methods to complex, integrated models for improved research outcomes.

Keywords:
Computer-aided drug discoveryData curationData extractionData integrationPharmacophore modelingQSARTRPV1

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Area of Science:

  • Computational chemistry and cheminformatics
  • Drug discovery and development
  • Pharmacology and toxicology

Background:

  • Publicly available large data sources like ChEMBLdb and Open PHACTS Discovery Platform streamline data acquisition.
  • Workflow engines such as KNIME and Pipeline Pilot facilitate complex data queries for multiple targets.

Purpose of the Study:

  • To demonstrate the integration of linked life science data into daily drug discovery projects.
  • To showcase the expansion of analytical approaches from conventional methods to complex, integrated multilayer models.

Main Methods:

  • Utilized large public databases (ChEMBLdb, Open PHACTS) for data set retrieval.
  • Employed workflow engines (KNIME, Pipeline Pilot) for complex querying and target searching.
  • Applied data to ligand- and structure-based studies, including P-gp inhibition, transporter selectivity, and TRPV1 modulation projects.

Main Results:

  • Efficient retrieval of consistent assay condition data for protein targets is now feasible.
  • Integration of linked life science data facilitated advanced computational modeling.
  • Successfully expanded research approaches beyond traditional Hansch analysis.

Conclusions:

  • The incorporation of linked life science data significantly enhances drug discovery workflows.
  • Advanced integrated multilayer models offer more comprehensive insights compared to conventional analyses.
  • This approach supports more sophisticated ligand- and structure-based studies for target modulation.