机器学习对人类片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, MD 20993, United States.
bioRxiv : the preprint server for biology
|April 22, 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,并发现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:
- 阿片类药物是片受体 (μOR) 的激动剂,而像纳洛这样的对抗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药物发现至关重要.
研究的目的:
- 开发和验证机器学习模型来分类μOR连接体内在活动.
- 为了评估一个三培训方案的实用性,以提高模型性能与有限的数据.
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2D分子描述器训练决策树 (额外树) 和传递信息的神经网络 (MPNN) 模型.
- 采用了三次训练方案,对额外的15816个未标记的配体存在分歧,以提高模型的稳定性.
主要成果:
- 树外和MPNN模型的保持测试AUC分别为91.5±3.9%和91.8±4.4%.
- 三次培训方案将MPNN模型的AUC提高到95.7%.
- 在化学空间内确定了主要的支架和结构相似的激动剂/对抗剂.
结论:
- 机器学习模型可以准确地预测μOR连接体内在的活动,即使数据集有限.
- 这种预测能力在公共安全风险评估和发现用于阿片类药物过量逆转的新疗法中具有潜在的应用.
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