机器学习对人类μ-阿片类受体中联结体内在活动的分类
Myongin Oh1,2, Maximilian Shen3, Ruibin Liu2
1Division of Applied Regulatory Science, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States.
ACS chemical neuroscience
|July 11, 2024
概括
机器学习模型使用分子数据准确预测阿片类受体活性. 这种方法有助于识别潜在的公共安全风险,并开发新的过量逆转治疗方法.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 阿片类药物是片类受体 (μOR) 的激动剂,而纳洛这样的激动剂可以逆转它们的作用.
- 精确分类μOR连接体活性对于安全性和治疗开发至关重要.
研究的目的:
- 开发机器学习模型来分类人类μOR.的连接体的内在活动 (激进/对抗).
- 评估不同ML模型的性能,并探索使用有限数据提高准确性的方法.
主要方法:
- 整理了983个小分子的数据库,并测量了人类μOR的Emax值.
- 使用SMILES字符串和分子描述符的训练决策树 (Extra-Tree) 和传递消息的神经网络 (MPNN) 模型.
- 采用半监督学习技术 (三级培训) 增加了15816个未标记的配体以提高模型性能.
主要成果:
- 外树和MPNN模型实现了高持久性测试AUC,分别为91.5%和91.8%.
- 三次训练显著改善了MPNN模型的性能,达到95.7%的AUC.
- 在数据集中确定了主要的化学支架和结构相似的agonists和antagonists.
结论:
- 机器学习模型可以准确地预测μOR连接体的内在活性,即使有有限的标记数据.
- 开发的模型显示出对评估未知的物质和发现新疗法剂的前景.
- 这项工作突出了ML在药物发现和与阿片类药物相关的公共安全应用中的潜力.
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