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Related Experiment Video

Updated: Dec 11, 2025

High-throughput Screening for Small-molecule Modulators of Inward Rectifier Potassium Channels
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Changing the HTS Paradigm: AI-Driven Iterative Screening for Hit Finding.

Gabriel H S Dreiman1,2, Magda Bictash1, Paul V Fish1

  • 1The Alzheimer's Research UK University College London Drug Discovery Institute, London, UK.

SLAS Discovery : Advancing Life Sciences R & D
|August 19, 2020
PubMed
Summary
This summary is machine-generated.

Iterative screening uses machine learning to select promising compounds in batches, improving hit finding and reducing compound numbers. This approach enhances drug discovery efficiency, even with limited iterations and resources.

Keywords:
AIHTSiterative screeningmachine learning

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Area of Science:

  • Drug Discovery
  • Computational Chemistry
  • Biotechnology

Background:

  • Iterative screening enhances compound selection by using machine learning on previous results.
  • Next-generation high-throughput screening (HTS) requires efficient methods due to complex assays.

Purpose of the Study:

  • To demonstrate the utility of iterative screening methods for drug discovery.
  • To analyze machine learning-based screening strategies using retrospective HTS data.

Main Methods:

  • Retrospective analysis of PubChem HTS data.
  • Application of machine learning models for compound selection in iterative screening.
  • Evaluation of screening strategies across different HTS experimental paradigms.

Main Results:

  • Screening 35% of compounds over three iterations yielded ~70% of active compounds.
  • Screening 50% of compounds yielded ~80% of active compounds.
  • Six iterations increased active compound returns to 78% and 90%.

Conclusions:

  • Iterative screening significantly improves hit finding efficiency in HTS.
  • Machine learning models executable on standard desktops are effective for iterative screening.
  • This approach offers a practical roadmap for implementing iterative screening in hit finding.