化学
样品(材料)
G蛋白偶联受体
建筑
计算生物学
人工智能
生物化学
受体
计算机科学
色谱法
生物
艺术
视觉艺术
作者
Shiming Chen,Feisheng Zhong
标识
DOI:10.1021/acs.jmedchem.4c01983
摘要
The quest for novel therapeutics targeting G protein-coupled receptors (GPCRs), essential in numerous physiological processes, is crucial in drug discovery. Despite the abundance of GPCR-targeting drugs, many receptors lack selective modulators, indicating a significant untapped therapeutic potential. To bridge this gap, we introduce GPCRSPACE, a novel GPCR-focused purchasable real chemical library developed using the G protein-coupled receptors large language models (GPCR LLM) architecture. Different from traditional machine learning models, GPCR LLM uses a positive sample machine learning strategy for training and does not need to construct any negative samples. This not only reduces false negatives but also reduces the time to label negative samples. GPCR LLM accelerates the identification and screening of potential GPCR-interactive compounds by learning the chemical space of GPCR-targeting molecules. GPCRSPACE, built on GPCR LLM, outperforms existing chemical data sets in synthesizability, structural diversity, and GPCR-likeness, making it a valuable tool for GPCR drug discovery.
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