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Quality over quantity: how to get the best results when using docking for repurposing.

Lenin Domínguez-Ramírez1, Maricruz Anaya-Ruiz2, Paulina Cortés-Hernández1

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Summary

Molecular docking programs were evaluated for identifying drug candidates. GNINA, using a convolutional neural network (CNN) score, excelled in ranking known ligands for phosphodiesterase 5A (PDE5A), improving drug discovery accuracy.

Keywords:
AutoDock VinaDock6GNINAUCSF ZINCconvolutional neural networkdockingdrug repurposingvirtual screening

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Molecular docking is a key computational method for studying protein-ligand interactions.
  • Assessing the reliability and accuracy of molecular docking results remains a challenge.
  • Identifying effective ligands for therapeutic targets is crucial for drug development.

Purpose of the Study:

  • To compare the performance of eight free molecular docking programs.
  • To evaluate the ability of these programs to identify known ligands for the human phosphodiesterase 5A (PDE5A) target.
  • To assess the utility of a convolutional neural network (CNN) score in improving docking result quality.

Main Methods:

  • Screening a drug library against the PDE5A target using eight different docking programs.
  • Utilizing Receiver Operating Characteristic (ROC) analysis to evaluate program specificity and sensitivity.
  • Implementing a CNN score cutoff to enhance the selection of high-quality docking results.
  • Comparing docking affinity scores with CNN scores for ranking potential drug binders.

Main Results:

  • GNINA demonstrated superior performance in identifying known PDE5A ligands, particularly when using its CNN score.
  • All tested docking suites exhibited limitations in specificity, often misidentifying non-binders.
  • Applying a CNN score cutoff of 0.9 significantly improved the specificity of results with minimal impact on sensitivity.
  • The refined dataset after cutoff was smaller but of higher quality, facilitating better candidate selection.

Conclusions:

  • GNINA's CNN score offers a valuable tool for improving the ranking and selection of potential drug candidates from molecular docking screens.
  • A heuristic approach combining ROC analysis and CNN score cutoffs can enhance the reliability of molecular docking for drug discovery.
  • The proposed method aids in producing more relevant docking results by filtering out false positives effectively.