基于深度学习方法的声敏剂-药物共组装构建（Small 40/2025）

IF 12.1 2区材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Small Pub Date : 2025-10-08 DOI:10.1002/smll.70744

Kanqi Wang, Liuyin Yang, Xiaowei Lu, Mingtao Cheng, Xiran Gui, Qingmin Chen, Yilin Wang, Yang Zhao, Dong Li, Gang Liu

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引用次数: 0

摘要

提出了一种基于人工智能的声敏剂-药物相互作用模型来构建共组装药物，准确率达到90.00%，召回率达到96.00%。烧蚀实验和梯度可视化分析了原子性质和分子结构对预测的影响。利用该模型，成功构建了甲氨蝶呤与大黄素组成的荧光成像引导肝癌治疗纳米药物。在第2502328号文章中，赵阳，李东，刘刚和同事。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Construction of Sonosensitizer-Drug Co-Assembly Based on Deep Learning Method (Small 40/2025)

查看原文本刊更多论文

Construction of Sonosensitizer-Drug Co-Assembly Based on Deep Learning Method (Small 40/2025)

Drug Co-Assemblies

An artificial intelligence-based sonosensitizer-drug interaction model was proposed to construct co-assembled drugs, achieving 90.00% accuracy and 96.00% recall. Ablation experiments and gradient visualization analyzed the impact of atomic properties and molecular structures on predictions. Using this model, a nanomedicine composed of methotrexate and emodin was successfully constructed for liver cancer treatment guided by fluorescence imaging. More in article number 2502328, Yang Zhao, Dong Li, Gang Liu, and co-workers.

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来源期刊

Small 工程技术-材料科学：综合

CiteScore

17.70

自引率

3.80%

发文量

1830

审稿时长

2.1 months

期刊介绍： Small serves as an exceptional platform for both experimental and theoretical studies in fundamental and applied interdisciplinary research at the nano- and microscale. The journal offers a compelling mix of peer-reviewed Research Articles, Reviews, Perspectives, and Comments. With a remarkable 2022 Journal Impact Factor of 13.3 (Journal Citation Reports from Clarivate Analytics, 2023), Small remains among the top multidisciplinary journals, covering a wide range of topics at the interface of materials science, chemistry, physics, engineering, medicine, and biology. Small's readership includes biochemists, biologists, biomedical scientists, chemists, engineers, information technologists, materials scientists, physicists, and theoreticians alike.