{Reference Type}: Journal Article
{Title}: Rapid Identification of Drug Mechanisms with Deep Learning-Based Multichannel Surface-Enhanced Raman Spectroscopy.
{Author}: Sun J;Lai W;Zhao J;Xue J;Zhu T;Xiao M;Man T;Wan Y;Pei H;Li L;
{Journal}: ACS Sens
{Volume}: 9
{Issue}: 8
{Year}: 2024 Aug 23
{Factor}: 9.618
{DOI}: 10.1021/acssensors.4c01205
{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.