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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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HTS-Oracle: A Retrainable AI Platform for High-Confidence Hit Identification Across Difficult-to-Drug Targets.

Hossam Nada, Laura Calvo-Barreiro, Sungwoo Cho

    Biorxiv : the Preprint Server for Biology
    |August 8, 2025
    PubMed
    Summary
    This summary is machine-generated.

    HTS-Oracle, an AI platform, significantly improves hit identification for difficult targets like CD28 by integrating deep learning and cheminformatics. This accelerates drug discovery, achieving an 8.4% hit rate, an eightfold increase over traditional methods.

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    Area of Science:

    • Computational drug discovery
    • Artificial intelligence in pharmacology
    • Molecular biology

    Background:

    • High-throughput screening (HTS) is crucial for drug discovery but often inefficient, with low hit rates (<2%) and data wastage.
    • Difficult-to-drug targets, such as the CD28 receptor, pose significant challenges for conventional screening methods.
    • Existing HTS methods like surface plasmon resonance (SPR) and affinity selection mass spectrometry (ASMS) can be costly and yield suboptimal results.

    Purpose of the Study:

    • To develop and validate HTS-Oracle, a novel deep learning platform for enhanced hit prediction in drug discovery.
    • To improve the efficiency and cost-effectiveness of identifying drug candidates for challenging biological targets.
    • To demonstrate the platform's capability in accelerating the discovery pipeline for immune co-stimulatory receptor CD28.

    Main Methods:

    • HTS-Oracle integrates transformer-derived molecular embeddings (ChemBERTa) with classical cheminformatics features using a multi-modal ensemble framework.
    • The platform was applied to screen a library of 1,120 small molecules against the CD28 target.
    • Experimental validation utilized temperature-related intensity change (TRIC) assays, followed by microscale thermophoresis (MST), ELISA, and molecular dynamics simulations for orthogonal confirmation.

    Main Results:

    • HTS-Oracle prioritized 345 candidates, leading to a validated hit rate of 8.4% (29 hits) via TRIC screening.
    • This represents an eightfold improvement in hit rate compared to conventional HTS methods.
    • Two identified compounds demonstrated disruption of the CD28-B7.1 interaction, confirmed by multiple orthogonal assays.

    Conclusions:

    • HTS-Oracle significantly enhances hit prediction accuracy and efficiency for difficult-to-drug targets.
    • The AI-driven platform streamlines the drug discovery process by enriching true positives and filtering non-binders upfront.
    • HTS-Oracle offers a scalable, validated AI framework to accelerate the identification of novel therapeutics for challenging targets.