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Related Experiment Video

Updated: Aug 23, 2025

Interactive Molecular Model Assembly with 3D Printing
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An open-source molecular builder and free energy preparation workflow.

Mateusz K Bieniek1, Ben Cree1, Rachael Pirie1

  • 1School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK.

Communications Chemistry
|November 2, 2022
PubMed
Summary

FEgrow is an open-source workflow that automates the creation of ligand binding poses for free energy calculations. This tool optimizes ligand conformations to improve predictions of binding free energies for drug discovery.

Keywords:
Computational chemistryStructure-based drug design

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Accurate prediction of protein-ligand binding free energies is crucial for drug discovery.
  • Automated methods for generating initial ligand binding poses are needed to facilitate free energy calculations.
  • Congeneric series of ligands offer a systematic approach to probe structure-activity relationships.

Purpose of the Study:

  • To introduce FEgrow, an open-source workflow for building congeneric series of ligands in protein binding pockets.
  • To enable automated generation and optimization of bioactive ligand conformations for free energy calculations.
  • To demonstrate the utility of FEgrow in predicting relative binding free energies.

Main Methods:

  • FEgrow enumerates and optimizes ligand conformations using hybrid machine learning/molecular mechanics potentials.
  • The gnina convolutional neural network scoring function is optionally used for scoring low-energy structures.
  • The workflow is applied to standard protein-ligand complex datasets and SARS-CoV-2 main protease inhibitors.

Main Results:

  • FEgrow successfully built and scored binding poses for ten congeneric series of ligands.
  • The workflow was used to retrospectively compute relative binding free energies for 13 SARS-CoV-2 main protease inhibitors.
  • The study demonstrates the practical application of FEgrow in drug discovery research.

Conclusions:

  • FEgrow provides a robust and user-friendly workflow for preparing ligands for free energy calculations.
  • The tool facilitates the generation of reliable binding poses, addressing a key challenge in computational drug design.
  • FEgrow is freely available, promoting wider adoption in the scientific community.