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Related Experiment Video

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High-throughput Functional Screening using a Homemade Dual-glow Luciferase Assay
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An integrated deep learning model accelerates luciferase based high throughput drug screening.

Xiaonan Zhang1, Xinxin Zhang1, Shuang Wang2

  • 1Key Laboratory of Marine Drugs, Ministry of Education of China, School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China; Marine Biomedical Research Institute of Qingdao, Qingdao 266071, China.

European Journal of Pharmaceutical Sciences : Official Journal of the European Federation for Pharmaceutical Sciences
|October 9, 2025
PubMed
Summary

We developed an integrated deep learning model to enhance high-throughput screening (HTS) for drug discovery. This AI-driven approach significantly improves accuracy and efficiency, reducing R&D costs and accelerating the identification of potential drug candidates.

Keywords:
Anti-inflammationAnti-metabolic syndromeAnti-tumorArtificial IntelligenceDeep learningHigh-throughput drug screening

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Biotechnology

Background:

  • High-throughput screening (HTS) accelerates drug development but faces challenges like high costs and time intensity.
  • Existing methods require extensive resources and manual labor, limiting efficiency.
  • Novel approaches are needed to optimize the drug discovery pipeline.

Purpose of the Study:

  • To develop an integrated deep learning model for enhanced HTS.
  • To identify patterns between compound characteristics and luciferase-based HTS values.
  • To accelerate the discovery of drug candidates for various diseases.

Main Methods:

  • Utilized ~100,000 HTS values from 18,840 compounds across five luciferase assays (STAT&NFκB, PPAR, P53, WNT, HIF).
  • Developed an integrated deep learning model correlating compound structure/molecular features with HTS results.
  • Performed AI-prediction for hit compounds, followed by in vitro and in vivo experimental validation.

Main Results:

  • The integrated AI model achieved superior classification performance compared to individual sub-models.
  • Screening accuracy and efficiency improved 7.08 to 32.04-fold versus conventional HTS.
  • Identified drug candidates (inhibitors/activators) with anti-inflammatory, anti-tumor, and anti-metabolic syndrome activities, including T4230 which inhibits inflammatory factors.

Conclusions:

  • The integrated AI-conducted HTS model significantly reduces R&D costs and accelerates drug development.
  • This AI-driven pipeline offers a valuable reference for artificial intelligence-accelerated specific signaling pathway-luciferase HTS.
  • The developed model demonstrates the potential of AI to revolutionize drug discovery efficiency.