Rapid Identification of Drug Mechanisms with Deep Learning-Based Multichannel Surface-Enhanced Raman Spectroscopy

被引：4

作者：

Sun, Jiajia ^{[1
]}

Lai, Wei ^{[2
]}

Zhao, Jiayan ^{[1
]}

Xue, Jinhong ^{[1
]}

Zhu, Tong ^{[1
]}

Xiao, Mingshu ^{[1
]}

Man, Tiantian ^{[3
]}

Wan, Ying ^{[3
]}

Pei, Hao ^{[1
]}

Li, Li ^{[1
]}

机构：

[1] East China Normal Univ, Shanghai Frontiers Sci Ctr Genome Editing & Cell T, Sch Chem & Mol Engn, Shanghai Key Lab Green Chem & Chem Proc, Shanghai 200241, Peoples R China

[2] Hubei Univ Automot Technol, Sch Math Phys & Optoelect Engn, Hubei Key Lab Energy Storage & Power Battery, Shiyan 442002, Peoples R China

[3] Nanjing Univ Sci & Technol, Sch Mech Engn, Nanjing 210094, Peoples R China

来源：

ACS SENSORS | 2024年 / 9卷 / 08期

关键词：

SERS; drug mechanisms; artificial nose; self-assembled monolayers; convolutional neural network; EXPRESSION SIGNATURES; METASTATIC CELLS; GOLD; SCATTERING; SERS; DIFFERENTIATION; CLASSIFICATION; PHENOTYPE; PROTEIN; FILMS;

D O I：

10.1021/acssensors.4c01205

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

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.

引用

页码：4227 / 4235

页数：9