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Default scoring functions often fail in virtual screening. Negative image-based rescoring (R-NiB) improves molecular docking pose ranking by comparing poses to the binding cavity

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Area of Science:

  • Computational chemistry and cheminformatics
  • Structure-based drug discovery

Background:

  • Default scoring functions in molecular docking struggle with virtual screening enrichment.
  • This limitation hinders the efficiency of structure-based drug discovery pipelines.
  • Existing rescoring and postprocessing methods show variable success, specificity, and cost.

Purpose of the Study:

  • To evaluate the efficacy of the negative image-based rescoring (R-NiB) methodology.
  • To assess R-NiB's performance when integrated with popular molecular docking software.
  • To determine R-NiB's suitability for large-scale virtual screening applications.

Main Methods:

  • The PANTHER/ShaEP-based R-NiB methodology was applied.
  • Tested integration with six docking programs: GLIDE, PLANTS, GOLD, DOCK, AUTODOCK, and AUTODOCK VINA.
  • Utilized five validated benchmark datasets for performance evaluation.

Main Results:

  • R-NiB consistently outperformed default scoring functions across all tested docking software.
  • The methodology demonstrated significant yield improvement in virtual screening.
  • R-NiB proved to be an inexpensive and effective postprocessing technique.

Conclusions:

  • The R-NiB methodology offers a robust solution to improve molecular docking accuracy.
  • R-NiB enhances virtual screening enrichment by leveraging binding pocket information.
  • The R-NiB approach is validated and ready for widespread adoption in drug discovery.