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  1. Home
  3. On The Difficulty To Rescore Hits From Ultralarge Docking Screens.
  1. Home
  3. On The Difficulty To Rescore Hits From Ultralarge Docking Screens.

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On the Difficulty to Rescore Hits from Ultralarge Docking Screens.

François Sindt1, Guillaume Bret1, Didier Rognan1

  • 1Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, France.

Journal of Chemical Information and Modeling
|May 22, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Virtual screening of massive chemical libraries shows promise, but accurately predicting binding affinity remains challenging. Post-screening analysis methods struggle to reliably identify potent and diverse drug candidates from millions of virtual hits.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • cheminformatics

Background:

  • Ultralarge virtual screening (ULVS) offers higher hit rates for potent ligands compared to conventional methods.
  • Efficient postprocessing is crucial for selecting diverse compounds from millions of virtual hits for synthesis and evaluation.

Purpose of the Study:

  • To retrospectively analyze the performance of various rescoring methods for binding affinity prediction in ULVS.
  • To evaluate the impact of energy refinement on different rescoring approaches.

Main Methods:

  • Analysis of ten ULVS hit lists against in vitro binding assays.
  • Testing eight rescoring methods: empirical, machine learning, molecular mechanics, and quantum mechanics.
  • Assessing the effect of energy refinement prior to rescoring.

Main Results:

  • No single rescoring method robustly distinguished active compounds from inactive ones across all assays.
  • Sophisticated methods generally performed better, but with limitations.
  • Energy refinement improved molecular and quantum mechanics but worsened empirical and machine learning predictions.

Conclusions:

  • Current rescoring methods are insufficient for reliably prioritizing compounds from ULVS.
  • Further development of robust binding affinity prediction tools is necessary for effective drug discovery pipelines.
  • Energy refinement strategies need careful consideration based on the chosen rescoring approach.