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变换DDS:一种可变的功能融合网络,用于药物之间的药物协同效应预测

Xinwei Zhao¹, Junqing Xu², Youyuan Shui¹

¹Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.

Journal of cheminformatics

|April 15, 2024

概括

确定协同作用的药物组合对于癌症治疗至关重要. 一个新的网络PermuteDDS有效地融合了药物和细胞系特征,以准确预测药物协同效应,优于现有方法.

科学领域:

计算生物学是一种计算生物学.
药物基因组学药物基因组学
生物信息学是一种生物信息学.

背景情况:

药物组合疗法在癌症治疗中表现有前途,但由于庞大的组合空间,识别协同组合具有挑战性.
目前用于药物协同作用预测的计算方法与药物对和细胞系特征的有效融合作斗争,限制了对复杂药物细胞系相互作用的理解.

研究的目的:

提出PermuteDDS,一个新的可变特征融合网络,用于准确的药物药物协同预测.
为了有效地融合各种药物和细胞系表征,使用可变的机制来捕捉复杂的相互作用.
为在癌症治疗中识别协同作用的药物组合提供一种有价值的计算工具.

主要方法:

开发了PermuteDDS,这是一个以多种药物和细胞系表示为输入的网络.
实施了可变的融合机制,通过在不同道中结合信息来整合药物和细胞系特征.
进行了比较和废弃实验,以验证该模型的有效性.

主要成果:

在两个基准药物药物协同效应数据集上,PermuteDDS实现了最先进的性能.
该模型在根据组织类型分层的独立测试集上展示了良好的概括性能.
实验结果证实了可变聚变机制在预测药物药物协同作用方面的有效性.

结论:

变换DDS是预测协同药物组合的有效和有价值的工具.
拟议的可变特征融合机制增强了对药物细胞系相互作用的理解.
该研究提供了一种强大的计算方法,以加速发现有效的组合疗法.

关键词:

癌症癌症癌症癌症药物组合药物组合是一种药物组合.可变的特征是核聚变.有协同效应的效应.

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