Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, School of Pharmacy, Fujian Medical University, Fuzhou 350122, China.
J Med Chem. 2024 Sep 26;67(18):16912-16922. doi: 10.1021/acs.jmedchem.4c01983. Epub 2024 Sep 17.
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.
针对在众多生理过程中发挥重要作用的 G 蛋白偶联受体（GPCR）的新型治疗方法的探索，在药物发现中至关重要。尽管有大量针对 GPCR 的药物，但许多受体缺乏选择性调节剂，这表明存在巨大的未开发治疗潜力。为了弥补这一差距，我们引入了 GPCRSPACE，这是一种新型的专注于 GPCR 的可购买真实化学库，使用 G 蛋白偶联受体大语言模型（GPCR LLM）架构开发。与传统的机器学习模型不同，GPCR LLM 使用正样本机器学习策略进行训练，并且不需要构建任何负样本。这不仅减少了假阴性，还减少了标记负样本的时间。GPCR LLM 通过学习靶向 GPCR 分子的化学空间，加速了潜在 GPCR 相互作用化合物的识别和筛选。基于 GPCR LLM 构建的 GPCRSPACE 在可合成性、结构多样性和 GPCR 相似性方面优于现有化学数据集，使其成为 GPCR 药物发现的有价值工具。
