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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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AutoRevDock: An open-source toolkit for scalable reverse docking.

Qing Luo1, Yuguang Mu2, Liangzhen Zheng3

  • 1Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao, China.

Protein Science : a Publication of the Protein Society
|October 22, 2025
PubMed
Summary
This summary is machine-generated.

AutoRevDock is a new open-source toolkit that speeds up reverse docking for drug discovery. It enhances target identification for repurposing and polypharmacology studies, improving accuracy and throughput.

Keywords:
drug repurposingdrug‐target interactionreverse dockingtarget fishingtarget prediction

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Reverse docking is crucial for drug repurposing and polypharmacology.
  • Existing tools face limitations in speed, accuracy, and accessibility.

Purpose of the Study:

  • Introduce AutoRevDock, an open-source Python toolkit to improve reverse docking.
  • Enhance throughput, accuracy, and local execution of reverse docking workflows.

Main Methods:

  • Integrated AutoDock Vina and idock with a hybrid Vina_SFCT scoring scheme.
  • Provided pre-processed human proteome and DrugBank target libraries.
  • Enabled custom target libraries and automated local execution.

Main Results:

  • idock demonstrated over 40x speed improvement compared to AutoDock Vina.
  • Vina_SFCT improved target identification for multi-target drugs.
  • Protein family information enhanced predictive power and hit rates.

Conclusions:

  • AutoRevDock provides a scalable, user-friendly solution for high-throughput target fishing.
  • The toolkit facilitates efficient drug repurposing and polypharmacology research.
  • Open-source availability promotes wider adoption in drug discovery.