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MolTarPred: A web tool for comprehensive target prediction with reliability estimation.

Antonio Peón1,2,3,4, Hongjian Li5,6, Ghita Ghislat7

  • 1Centre de Recherche en Cancérologie de Marseille (CRCM), U1068, Inserm, Marseille, France.

Chemical Biology & Drug Design
|March 28, 2019
PubMed
Summary
This summary is machine-generated.

MolTarPred is a new web tool for predicting protein targets of small organic compounds. It offers a reliability score to prioritize experimental validation, improving hit rates in drug discovery.

Keywords:
polypharmacology predictiontarget deconvolutiontarget fishingtarget predictionwebserver

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • In silico molecular target prediction is crucial for understanding drug efficacy and side effects.
  • Existing prediction methods often lack web accessibility, reliability estimation, and comprehensive validation.
  • Identifying off-target effects (polypharmacology) is essential in drug development.

Purpose of the Study:

  • To introduce MolTarPred, a user-friendly web tool for predicting protein targets of small organic compounds.
  • To provide a reliable method for estimating the confidence of target predictions.
  • To facilitate the understanding of molecular polypharmacology.

Main Methods:

  • Development of a web tool (MolTarPred) utilizing a large knowledge base from the ChEMBL database.
  • Inclusion of 607,659 compounds and 4,553 macromolecular targets.
  • Implementation of a reliability scoring system for predictions.

Main Results:

  • MolTarPred provides target predictions for small molecules within approximately one minute.
  • The tool displays predicted targets, similar compounds, and their associated targets for visual comparison.
  • A reliability score is generated for each prediction, aiding in experimental prioritization.

Conclusions:

  • MolTarPred is a valuable resource for scientists needing to understand compound polypharmacology.
  • The reliability score enhances experimental efficiency by focusing on high-confidence predictions.
  • This tool can lead to higher prospective hit rates in drug discovery efforts.