GPCR-Filter：用于高效精准GPCR调节剂发现的深度学习框架

摘要

G蛋白偶联受体（GPCRs）调控多种生理过程，是现代药理学研究的核心。然而，由于受体激活常源于复杂的变构效应而非直接结合亲和力，且传统检测方法速度慢、成本高、难以捕捉这些动态过程，GPCR调节剂的发现仍面临挑战。本文提出GPCR-Filter——一个专为GPCR调节剂发现而开发的深度学习框架。我们整合了超过9万个经实验验证的GPCR-配体对数据构建高质量数据集，为模型训练与评估提供坚实基础。该框架融合ESM-3蛋白语言模型生成高保真GPCR序列表征，通过基于注意力的融合机制与编码配体结构的图神经网络相耦合，从而学习受体-配体功能关系。在多种评估场景下，GPCR-Filter持续优于当前最先进的化合物-蛋白质相互作用模型，并对未见过受体和配体表现出强大泛化能力。值得注意的是，该模型成功识别出具有独特化学框架的5-HT1A受体微摩尔级激动剂。这些成果确立了GPCR-Filter作为可扩展的高效计算方法在GPCR调节剂发现中的应用价值，推动了针对复杂信号系统的AI辅助药物研发进程。

English

G protein-coupled receptors (GPCRs) govern diverse physiological processes and are central to modern pharmacology. Yet discovering GPCR modulators remains challenging because receptor activation often arises from complex allosteric effects rather than direct binding affinity, and conventional assays are slow, costly, and not optimized for capturing these dynamics. Here we present GPCR-Filter, a deep learning framework specifically developed for GPCR modulator discovery. We assembled a high-quality dataset of over 90,000 experimentally validated GPCR-ligand pairs, providing a robust foundation for training and evaluation. GPCR-Filter integrates the ESM-3 protein language model for high-fidelity GPCR sequence representations with graph neural networks that encode ligand structures, coupled through an attention-based fusion mechanism that learns receptor-ligand functional relationships. Across multiple evaluation settings, GPCR-Filter consistently outperforms state-of-the-art compound-protein interaction models and exhibits strong generalization to unseen receptors and ligands. Notably, the model successfully identified micromolar-level agonists of the 5-HT1A receptor with distinct chemical frameworks. These results establish GPCR-Filter as a scalable and effective computational approach for GPCR modulator discovery, advancing AI-assisted drug development for complex signaling systems.

GPCR-Filter：用于高效精准GPCR调节剂发现的深度学习框架

GPCR-Filter: a deep learning framework for efficient and precise GPCR modulator discovery

摘要

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