Abstract：
Rapid identification of drug mechanisms is vital to the development and effective use of chemotherapeutics. Herein, we develop a multichannel surface-enhanced Raman scattering (SERS) sensor array and apply deep learning approaches to realize the rapid identification of the mechanisms of various chemotherapeutic drugs. By implementing a series of self-assembled monolayers (SAMs) with varied molecular characteristics to promote heterogeneous physicochemical interactions at the interfaces, the sensor can generate diversified SERS signatures for directly high-dimensionality fingerprinting drug-induced molecular changes in cells. We further train the convolutional neural network model on the multidimensional SAM-modulated SERS data set and achieve a discriminatory accuracy toward 99%. We expect that such a platform will contribute to expanding the toolbox for drug screening and characterization and facilitate the drug development process.
摘要：
药物机制的快速鉴定对于化学疗法的开发和有效使用至关重要。在这里，我们开发了一种多通道表面增强拉曼散射（SERS）传感器阵列，并应用深度学习方法来实现对各种化疗药物机制的快速识别。通过实施一系列具有不同分子特征的自组装单层（SAM），以促进界面处的异质物理化学相互作用，该传感器可以生成多样化的SERS特征，用于直接进行高维指纹识别药物诱导的细胞分子变化。我们在多维SAM调制的SERS数据集上进一步训练卷积神经网络模型，并达到99％的判别精度。我们希望这样的平台将有助于扩展药物筛选和表征的工具箱，并促进药物开发过程。
