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使用卷积神经网络在TEM图像中描述DNA原始结构纳米结构

Xingfei Wei¹, Qiankun Mo¹, Chi Chen²

¹Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States.

Journal of chemical information and modeling

|June 20, 2025

概括

卷积神经网络 (CNN) 模型现在可以描述DNA原始结构纳米结构. 精心调整的VGG16模型在传输电子显微镜图像中识别结合位点时显示出高精度.

科学领域:

纳米技术纳米技术
生物医学工程生物医学工程
计算科学计算科学

背景情况:

人工智能 (AI) 模型加速材料设计.
在生物医学中,DNA原始结构纳米结构对于可编程自组装至关重要.
描述纳米结构有助于理解它们的功能和应用.

研究的目的:

为了对九个卷积神经网络 (CNN) 模型的性能进行基准测试,用于表征DNA原始结构纳米结构.
评估CNN模型在传输电子显微镜 (TEM) 图像中确定结合数的能力.
使用模拟和实验数据,比较预训练和微调CNN模型的准确性.

主要方法:

在720个粗粒度分子动力学 (MD) 模拟图像上预训练了9个CNN模型 (AlexNet,GoogLeNet,VGG16,VGG19,ResNet18,ResNet34,ResNet50,ResNet101,ResNet152).
微调预训练CNN模型,使用146张实验TEM图像的数据集.
基于准确性,计算时间和模型大小的基准模型性能.

主要成果:

所有的CNN模型都表现出类似的计算时间要求.
在预训练模型中,ResNet50和VGG16在测试MD图像上取得了最高的准确性.
精心调整的VGG16与实验TEM图像达到了最高的一致性,表明了卓越的表征能力.

结论:

精心调整的VGG16 CNN模型可以从TEM图像中高效准确地描述DNA原木纳米结构中的结合位数.
这种由人工智能驱动的方法为分析大型图像数据集中的纳米结构提供了一种快速的方法.
该研究强调了人工智能在推进材料设计和纳米医学应用方面的潜力.

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