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Updated: Sep 5, 2025

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DockingPie: a consensus docking plugin for PyMOL.

Serena Rosignoli1, Alessandro Paiardini1

  • 1Department of Biochemical Sciences 'A. Rossi Fanelli', Sapienza Università di Roma, Rome 00185, Italy.

Bioinformatics (Oxford, England)
|July 6, 2022
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Summary
This summary is machine-generated.

DockingPie is a new PyMOL plugin that simplifies protein-ligand docking and consensus docking analyses. It integrates four popular docking engines for improved drug discovery predictions.

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Area of Science:

  • Computational chemistry
  • Structural bioinformatics
  • Drug discovery

Background:

  • Protein-ligand docking is a key method in computer-aided drug discovery for predicting molecular interactions.
  • The variety of docking algorithms and scoring functions presents challenges in achieving consistent accuracy and reproducibility.
  • Consensus docking approaches are emerging to address these limitations.

Purpose of the Study:

  • To develop an accessible tool for performing both individual and consensus protein-ligand docking analyses.
  • To integrate multiple popular docking engines into a single, user-friendly platform.
  • To enhance the efficiency and reliability of virtual screening studies.

Main Methods:

  • Development of DockingPie, an open-source plugin for PyMOL.
  • Integration of four docking engines: Smina, AutoDock Vina, ADFR, and RxDock.
  • Implementation of a graphical user interface (GUI) within PyMOL for intuitive operation.

Main Results:

  • DockingPie provides an integrated environment for executing docking analyses.
  • The plugin supports individual and consensus docking strategies.
  • It offers an easy-to-use interface for researchers to perform complex docking studies.

Conclusions:

  • DockingPie facilitates the application of consensus docking techniques.
  • The tool enhances the capabilities of PyMOL for drug discovery research.
  • It offers a streamlined approach to protein-ligand binding mode prediction and virtual screening.