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Leveraging Consensus Docking Approaches for Human Mitochondrial Complexes I and III.

Karin Grillberger1, Viktoria Magel2, Marcel Leist2

  • 1Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, Vienna 1090, Austria.

Chemical Research in Toxicology
|December 30, 2025
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Summary
This summary is machine-generated.

Structure-based molecular docking effectively predicts toxicity by ranking compound hazards. This method, particularly consensus scoring, addresses challenges like activity cliffs missed by traditional QSAR, aiding in prioritizing safer chemical compounds.

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Area of Science:

  • Computational Chemistry
  • Toxicology
  • Drug Discovery

Background:

  • Structure-based methods like molecular docking are underutilized for toxicity prediction.
  • Conventional Quantitative Structure-Activity Relationship (QSAR) methods struggle with challenges like activity cliffs caused by stereoisomerism.

Purpose of the Study:

  • To evaluate molecular docking scoring functions and interaction fingerprints for predicting the toxicity of compounds targeting human mitochondrial complexes I and III (CI, CIII).
  • To assess the utility of these structure-based methods in identifying activity cliffs and prioritizing compounds for safety.

Main Methods:

  • Induced fit docking protocol to model binding site flexibility.
  • Application of various docking scoring functions and protein-ligand interaction fingerprints.
  • Binding energy minimization for rescoring.
  • Analysis of rank correlation with experimental data and in vitro testing.

Main Results:

  • Both individual and consensus docking scoring showed acceptable rank correlation with experimental data for CIII.
  • Consensus interaction fingerprints differentiated inhibitor subtypes at CIII.
  • In vitro tests confirmed an isomerism-dependent activity cliff for E-/Z-Fenpyroximate at CI.

Conclusions:

  • Consensus docking and scoring serve as valuable screening tools for prioritizing compounds based on predicted binding affinities.
  • These structure-based approaches enhance toxicity prediction, especially for complex cases like stereoisomerism-driven activity cliffs.