Anesthetics are crucial in surgical procedures and therapeutic interventions, but they come with side effects and varying levels of effectiveness, calling for novel anesthetic agents that offer more precise and controllable effects. Targeting Gamma-aminobutyric acid (GABA) receptors, the primary inhibitory receptors in the central nervous system, could enhance their inhibitory action, potentially reducing side effects while improving the potency of anesthetics. In this study, we introduce a proteomic learning of GABA receptor-mediated anesthesia based on 24 GABA receptor subtypes by considering over 4000 proteins in protein-protein interaction (PPI) networks and over 1.5 millions known binding compounds. We develop a corresponding drug-target interaction network to identify potential lead compounds for novel anesthetic design. To ensure robust proteomic learning predictions, we curated a dataset comprising 136 targets from a pool of 980 targets within the PPI networks. We employed three machine learning algorithms, integrating advanced natural language processing (NLP) models such as pretrained transformer and autoencoder embeddings. Through a comprehensive screening process, we evaluated the side effects and repurposing potential of over 180,000 drug candidates targeting the GABRA5 receptor. Additionally, we assessed the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of these candidates to identify those with near-optimal characteristics. This approach also involved optimizing the structures of existing anesthetics. Our work presents an innovative strategy for the development of new anesthetic drugs, optimization of anesthetic use, and deeper understanding of potential anesthesia-related side effects.

翻译：麻醉剂在外科手术和治疗干预中至关重要，但其伴随副作用且疗效存在差异，因此需要开发具有更精确可控效果的新型麻醉剂。以γ-氨基丁酸（GABA）受体——中枢神经系统主要抑制性受体——为靶点，可增强其抑制作用，有望在提升麻醉效力的同时减少副作用。本研究基于24种GABA受体亚型，通过分析蛋白质-蛋白质相互作用（PPI）网络中超过4000种蛋白质及逾150万种已知结合化合物，建立了GABA受体介导麻醉的蛋白质组学学习框架。我们构建了相应的药物-靶点相互作用网络，以识别用于新型麻醉剂设计的潜在先导化合物。为确保蛋白质组学学习预测的稳健性，我们从PPI网络的980个靶点中筛选出136个靶点构建数据集。我们采用三种机器学习算法，并整合了预训练Transformer与自编码器嵌入等先进自然语言处理（NLP）模型。通过系统筛选流程，评估了针对GABRA5受体的超过18万种候选药物的副作用及再利用潜力。此外，我们通过ADMET（吸收、分布、代谢、排泄和毒性）特性评估，筛选出具有接近最优特性的候选化合物。该方法还涉及对现有麻醉剂结构的优化。本研究为新型麻醉药物开发、麻醉使用优化及深入理解潜在麻醉相关副作用提供了创新策略。