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DTLCDR:基于目标的多式联接深度学习框架,用于预测癌症药物反应

Jie Yu¹, Cheng Shi¹, Yiran Zhou¹

¹State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China.

Journal of pharmaceutical analysis

|October 6, 2025

概括

一个新的深度学习模型,DTLCDR,改善了细胞系和临床环境中癌症药物反应的预测. 它通过整合药物向相互作用和单细胞基因组知识来提高新药的通用性.

科学领域:

计算生物学是一种计算生物学.
药理学药理学是指药理学的学科.
医学中的人工智能

背景情况:

准确预测癌症药物反应对于个性化医学至关重要.
现有的计算方法难以将其推广到新的,未见的药物.
从细胞系数据预测临床药物反应仍然是一个挑战.

研究的目的:

开发一种深度学习模型,用于预测临床前和临床癌症药物反应 (CDR).
提高药物反应预测模型对新药的普遍性.
在细胞系和临床数据集上验证模型的性能.

主要方法:

提出了一个多式联接深度学习模型,DTLCDR.
综合化学描述符,分子图表,药物向配置文件和单细胞表达数据.
利用药物向相互作用模型和预训练的单细胞语言模型.

主要成果:

与基线方法相比,DTLCDR在预测对未见药物的反应方面表现出卓越的概括性和稳定性.
废弃性研究证实了药物向信息的重要贡献.
在体外实验中验证了DTLCDR能够预测新分子有效性的能力.
该模型在临床数据集上表现出令人满意的表现,即使对于不在细胞系训练集中的药物.

结论:

DTLCDR是个性化药物发现和开发的一个有前途的工具.
整合药物向相互作用和单细胞基因组知识可以提高药物反应预测的概括性.
该模型显示了预测临床前和临床药物疗效的真实应用的潜力.

关键词:

癌症药物反应反应癌症药物反应深度学习是一种深度学习.药物-标药物相互作用多式联络融合是多式联络的融合方式.个性化医疗是个性化的医疗.单单元格语言模型

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