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Attracting Cavities 2.0: Improving the Flexibility and Robustness for Small-Molecule Docking.

Ute F Röhrig1, Mathilde Goullieux1, Marine Bugnon1

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The improved Attracting Cavities 2.0 (AC 2.0) algorithm enhances molecular docking accuracy and flexibility. AC 2.0 outperforms other methods in redocking and blind docking, offering robust sampling for drug discovery.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Molecular docking predicts ligand-macromolecule binding.
  • Existing algorithms like GOLD and AutoDock Vina have limitations.
  • The Attracting Cavities (AC) algorithm previously showed competitive performance.

Purpose of the Study:

  • To introduce and evaluate AC 2.0, an enhanced molecular docking algorithm.
  • To improve sampling robustness and docking flexibility (speed vs. accuracy).
  • To benchmark AC 2.0 against established methods using PDBbind.

Main Methods:

  • Utilized the PDBbind Core set (2016 version) with 285 complexes for benchmarking.
  • Assessed performance in redocking, blind docking, and cross-docking scenarios.
  • Evaluated scoring function accuracy and enrichment factors in virtual screening.

Main Results:

  • AC 2.0 achieved a 73.3% success rate in redocking, surpassing GOLD (63.9%) and Vina (58.0%).
  • Demonstrated strong performance in blind docking due to its force-field scoring and sampling.
  • Showed a 42.5% success rate in cross-docking, comparable to GOLD and superior to Vina.

Conclusions:

  • AC 2.0 offers significant improvements in molecular docking accuracy and efficiency.
  • The algorithm's robustness and flexibility make it suitable for various docking tasks.
  • AC 2.0 aids in identifying potential drug candidates and evaluating experimental data quality.