整合qHTS和QSAR模型以识别安全的GPCR向化合物:专注于hERG依赖性心脏毒性
Xi Luo1, Jinghua Zhao1, Srilatha Sakamuru1
1Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States.
Journal of chemical information and modeling
|February 17, 2026
概括
这项研究开发了机器学习模型,以识别具有较低心脏风险的G蛋白结合受体 (GPCR) 药物. 这些模型预测了针对GPCRs的化合物,同时最大限度地减少了hERG通道相互作用,提高了药物安全性.
科学领域:
- 药理学和毒理学 药理学和毒理学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- G蛋白结合受体 (GPCRs) 是许多疾病的关键药物标.
- 药物抑制hERG通道可能导致危及生命的心律失常.
- 评估GPCR-hERG相互作用对于安全的药物开发至关重要.
研究的目的:
- 为了识别具有减少hERG责任性的新型GPCR调制剂.
- 开发和验证用于预测GPCR活动和hERG安全性的机器学习模型.
- 为更安全的药物发现提供高效的策略.
主要方法:
- 对 Tox21 10K 化合物库的 GPCR 活性进行量化高通量选 (qHTS).
- 基于机器学习 (ML) 的定量结构-活动关系 (QSAR) 模型的开发和应用.
- 虚拟选约36万种化合物,并对顶级预测进行实验验证.
主要成果:
- 识别了具有最小hERG责任的选择性GPCR激素和抑制剂.
- ML-QSAR模型准确地预测了具有降低hERG风险的GPCR向化合物.
- 使用LOPAC验证模型,并确定了具有所需配置文件的新型化合物.
结论:
- 基于机器学习的QSAR模型为发现GPCR调节器提供了一种高效的方法.
- 这一策略有效地降低了与hERG通道抑制相关的心脏风险.
- 这些发现支持开发针对GPCRs的更安全的治疗方法.
相关概念视频
01:28Structure-Activity Relationships and Drug Design
1.9K
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
1.9K
01:29Pharmacogenomics: Identification of New Drug Targets
20
Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
20
01:11Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase
24
Genetic polymorphisms in drug targets have emerged as critical determinants of interindividual variability in drug response and toxicity. Pharmacogenomic investigations increasingly focus on identifying these variations to personalize and optimize therapeutic interventions. A drug target may be a receptor, enzyme, or signaling protein involved in pharmacologic responses or disease-related pathways. While early pharmacogenetic studies focused primarily on drug metabolism, current research...
24
01:15Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions
22
PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure...
22