GPCR-Filter: a deep learning framework for efficient and precise GPCR modulator discovery
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| Format: | Preprint |
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2026
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| author | Ning, Jingjie Shen, Xiangzhen Hou, Li Shen, Shiyi Yang, Jiahao Li, Junrui Shan, Hong Wu, Sanan Gao, Sihan Xu, H. Eric He, Xinheng |
| author_facet | Ning, Jingjie Shen, Xiangzhen Hou, Li Shen, Shiyi Yang, Jiahao Li, Junrui Shan, Hong Wu, Sanan Gao, Sihan Xu, H. Eric He, Xinheng |
| contents | 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-HT\textsubscript{1A} 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19149 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GPCR-Filter: a deep learning framework for efficient and precise GPCR modulator discovery Ning, Jingjie Shen, Xiangzhen Hou, Li Shen, Shiyi Yang, Jiahao Li, Junrui Shan, Hong Wu, Sanan Gao, Sihan Xu, H. Eric He, Xinheng Machine Learning Quantitative Methods 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-HT\textsubscript{1A} 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. |
| title | GPCR-Filter: a deep learning framework for efficient and precise GPCR modulator discovery |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2601.19149 |