标签作为一个特征:网络同类性用于系统地注释人类GPCR药物标相互作用
Frederik G Hansson1, Niklas Gesmar Madsen1, Lea G Hansen2
1The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark.
Nature communications
|May 2, 2025
概括
这项研究介绍了化学空间神经网络,这是一种用于预测药物向相互作用的新型机器学习模型. 该模型通过利用网络同类性和整合标记数据来提高预测准确性,改善药物安全和发现.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 机器学习是机器学习.
背景情况:
- 人类G蛋白结合受体是FDA批准的药物的关键标,但综合性药物标相互作用测试受到成本和技术障碍的限制.
- 药物的尚未研究的非目标效应对患者安全构成重大风险.
- 传统的药物发现模型往往专注于探索新的化学空间,而不是优化已知的空间内的预测.
研究的目的:
- 开发一种新的机器学习模型,化学空间神经网络 (CSNN),用于准确预测药物向相互作用.
- 调查网络同类性和标签作为增强分布内预测准确性的特征的作用.
- 在高通量实验系统中验证CSNN模型,以发现新的药物向相互作用.
主要方法:
- 开发了一个名为化学空间神经网络 (CSNN) 的邻里到预测模型.
- 利用网络同类性和无训练的图形神经网络,用标签作为特征.
- 在推断过程中集成标记数据以提高预测准确度.
- 使用高通量酵母生物传感系统验证了模型,其中有3773种药物向相互作用,539种化合物和7种人类G蛋白结合受体.
主要成果:
- CSNN的预测准确性与网络同类性有很强的相关性.
- 使用标签作为特征显著提高了机器学习模型的分布式预测能力.
- 该模型成功地在实验系统内确定了FDA批准的药物的新药向相互作用.
结论:
- 化学空间神经网络提供了一种可靠的方法,以提高药物向相互作用的分布式预测准确性.
- 利用网络同类性和标记数据对于建立强大的药物发现预测模型至关重要.
- 这项工作为指导实验验证和扩大对药物向相互作用的理解提供了基础,最终改善了药物安全和发现管道.
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