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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Updated: Jun 9, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Target Fisher: A Consensus Structure-Based Target Prediction Tool, and its Application in the Discovery of Selective

Julián F Fernández1,2, Leandro Martinez Heredia3, Fernando Caracciolo1,2

  • 1Departamento de Quimica Organica, Facultad de Ciencias Exactas, Universidad de Buenos Aires, Intendente Guiraldes 2160, Buenos Aires, Argentina.

Chemistry (Weinheim an Der Bergstrasse, Germany)
|October 24, 2024
PubMed
Summary

Target Fisher is a novel tool that combines molecular docking and machine learning for predicting biological targets. This structure-based approach aids in drug discovery and repurposing, as shown by identifying MAO-B inhibitors for neurodegenerative diseases.

Keywords:
CoumarinsDockingMAOBMachine learningTarget prediction

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Area of Science:

  • Computational chemistry and cheminformatics
  • Drug discovery and medicinal chemistry
  • Bioinformatics and systems biology

Background:

  • Identifying specific biological targets is crucial for effective drug discovery and repurposing.
  • Existing methods for target prediction can be limited in scope or accessibility.
  • Optimizing the use of bioassays requires accurate prediction of potential molecular targets.

Purpose of the Study:

  • To introduce Target Fisher, a consensus structure-based target prediction tool.
  • To integrate molecular docking and machine learning for enhanced target identification.
  • To provide a user-friendly web server for accessing predictions on 37 protein targets.

Main Methods:

  • Utilizing per-residue energy decomposition profiles from docking poses as molecular fingerprints.
  • Training target-specific machine learning models using these fingerprints.
  • Developing a web server for accessible predictions and a curated set of 37 protein targets.

Main Results:

  • Target Fisher successfully predicts potential biological targets by integrating docking and machine learning.
  • A case study demonstrated the tool's efficacy in identifying selective inhibitors of monoamine oxidase B (MAO-B) for neurodegenerative diseases.
  • Experimental validation confirmed the identified MAO-B inhibitors, showcasing the tool's practical utility.

Conclusions:

  • Target Fisher is a valuable tool for organic and medicinal chemistry groups in target identification, drug discovery, and drug repurposing.
  • The tool's structure-based approach and machine learning integration offer a powerful method for predicting drug targets.
  • The successful identification and validation of MAO-B inhibitors highlight the practical impact of Target Fisher in addressing neurodegenerative diseases.