Nada Hossam, Calvo-Barreiro Laura, Cho Sungwoo, Upadhyay Saurabh, Meanwell Nicholas A, Gabr Moustafa T
bioRxiv. 2025 Jul 25:2025.07.21.666047. doi: 10.1101/2025.07.21.666047.
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.
尽管计算药物发现领域取得了快速进展，但高通量筛选（HTS）仍然是识别初始活性化合物的主要方法，特别是对于小分子可处理性有限的靶点。然而，传统的高通量筛选活动成本高昂且效率低下，通常命中率低于2%，并且会丢弃有价值的阴性数据。在此，我们展示了HTS-Oracle，这是一个基于深度学习的可重新训练平台，它在多模态集成框架中将源自Transformer的分子嵌入（ChemBERTa）与经典化学信息学特征相结合，用于活性预测。我们将HTS-Oracle应用于免疫共刺激受体CD28，这是一个典型的难以成药的靶点，并从一个包含1120个小分子的化学多样性文库中筛选出345个候选物。通过与温度相关的强度变化（TRIC）进行实验筛选，确定了29个活性化合物（命中率8.4%），相较于表面等离子体共振（SPR）、TRIC以及基于亲和选择质谱（ASMS）的高通量筛选等传统方法，提高了八倍。通过预先富集真阳性并过滤掉非结合物，HTS-Oracle简化了发现流程，并实现了更具针对性、成本效益更高的筛选。两种活性化合物破坏了CD28-B7.1相互作用，并通过微量热泳动（MST）、酶联免疫吸附测定（ELISA）和分子动力学模拟提供了正交验证。HTS-Oracle减轻了筛选负担并提高了发现效率，为加速针对难以成药靶点的活性化合物识别提供了一个强大、可扩展且经过实验验证的人工智能框架。
