GPCR-Filter: a deep learning framework for efficient and precise GPCR modulator discovery
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.
697986c2df44b75fa47e47e7
Jingjie Ning
claimed_verified
2026-01-28T11:15:58.913Z
6310a812a23f0327bce68778
https://cdn-avatars.huggingface.co/v1/production/uploads/6310a812a23f0327bce68778/dp9JherZV-RYiSRKnvxsh.jpeg
Ethan Ning
ethanning
user
ethanning
697986c2df44b75fa47e47e8
Xiangzhen Shen
697986c2df44b75fa47e47e9
Li Hou
697986c2df44b75fa47e47ea
Shiyi Shen
697986c2df44b75fa47e47eb
Jiahao Yang
697986c2df44b75fa47e47ec
Junrui Li
697986c2df44b75fa47e47ed
Hong Shan
697986c2df44b75fa47e47ee
Sanan Wu
697986c2df44b75fa47e47ef
Sihan Gao
697986c2df44b75fa47e47f0
Huaqiang Eric Xu
697986c2df44b75fa47e47f1
Xinheng He
GPCR-Filter: a deep learning framework for efficient and precise GPCR modulator discovery
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 is a deep learning framework that combines protein language models and graph neural networks to identify GPCR modulators with high accuracy and generalization across unseen receptors and ligands.
GPCR deep learning framework ESM-3 protein language model graph neural networks attention-based fusion mechanism compound-protein interaction models receptor-ligand functional relationships