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Meanwell, N.

Publications and source records attributed to Meanwell, N..

2 recordsLinked to original sources
bioRxiv2025

HTS-Oracle: Experimentally validated AI-enabled prioritization for generalizable small molecule hit discovery

High-throughput screening (HTS) remains a central pillar of small molecule discovery yet routinely fails for immune receptors and protein-protein interaction-driven targets. Here, we introduce HTS-Oracle, an experimentally validated AI system for prospective hit discovery that integrates molecular language modeling with cheminformatics to prioritize bioactive compounds at scale. We deploy HTS-Oracle across three clinically validated yet historically intractable immune targets, TREM2, CHI3L1, and CD28, representing cryptic binding pockets, intrinsically disordered proteins, and protein-protein interaction-driven immune checkpoint, respectively. Across the tested targets, HTS-Oracle reduces experimental screening requirements by up to >99% while increasing hit rates by up to 176-fold relative to traditional HTS. Notably, the platform remains predictive under extreme data sparsity, achieving an eightfold improvement for CD28 despite fewer than 2% actives in training. By consistently enriching for experimentally validated hits, HTS-Oracle establishes a new performance benchmark for hit discovery and unlocks small molecule access to immune targets long regarded as chemically inaccessible.

bioinformatics↗
bioRxiv2025

HTS-Oracle: A Retrainable AI Platform for High-Confidence Hit Identification Across Difficult-to-Drug Targets

Despite rapid advances in computational drug discovery, high-throughput screening (HTS) remains the primary method for identifying initial hits, particularly for targets with limited tractability to small molecules. Yet conventional HTS campaigns are costly and inefficient, often yielding hit rates below 2% and discarding valuable negative data. Here we present HTS-Oracle, a retrainable, deep learning-based platform that integrates transformer-derived molecular embeddings (ChemBERTa) with classical cheminformatics features in a multi-modal ensemble framework for hit prediction. We applied HTS-Oracle to the immune co-stimulatory receptor CD28, a prototypical difficult-to-drug target, and prioritized 345 candidates from a chemically diverse library of 1,120 small molecules. Experimental screening via temperature-related intensity change (TRIC) identified 29 hits (8.4% hit rate), representing an eightfold improvement over conventional methods such as surface plasmon resonance (SPR), TRIC, and affinity selection mass spectrometry (ASMS)-based HTS. By enriching true positives and filtering out non-binders upfront, HTS-Oracle streamlines the discovery pipeline and enables more focused, cost-effective screening. Two hit compounds disrupted the CD28-B7.1 interaction, with orthogonal validation provided by MST, ELISA, and molecular dynamics simulations. HTS-Oracle reduces screening burden and improves discovery efficiency, offering a powerful, scalable, and experimentally validated AI framework for accelerating hit identification across difficult-to-drug targets.

bioinformatics↗