Drug screening methods based on deep learning

Cheng Zhang; Jianhua Wang; Hongxin Lin; Zhenlin Zhan; Lisheng Lin; Jian Sui

doi:10.1117/12.2638221

15 November 2022 Drug screening methods based on deep learning

Cheng Zhang, Jianhua Wang, Hongxin Lin, Zhenlin Zhan, Lisheng Lin, Jian Sui

Proceedings Volume 12448, 5th Optics Young Scientist Summit (OYSS 2022); 124480V (2022) https://doi.org/10.1117/12.2638221
Event: 5th Optics Young Scientist Summit (OYSS 2022), 2022, Fuzhou, China

Abstract

Drug screening is an important step in the development of new drugs. Through appropriate experimental methods and screening models, drugs with specific bioactivity can be transferred from laboratory research to clinical application. Nowadays, traditional efficiency of drug experimentation has been unable to meet the needs of our society. With the rapid development of computer technology, computer-assisted diagnosis and treatment have been gradually accepted and recognized by clinicians and patients. Drug screening process at the cellular level was studied in this paper. We not only compared the advantages and disadvantages of deep learning models and traditional machine learning methods, but also analyzed the performance of different deep learning models. First, Hela cells injected with different anti-stress drugs were divided into groups for experiment. G3BP, TIA-1 and the nucleus were labeled, respectively. The images were obtained using a single-photon microscope. Then, we distinguished the images of Hela cells treated with different drugs through visual observation, traditional machine learning (LBP/Gabor+SVM) and deep learning algorithms (VGGNet, GoogLeNet, ResNext and DenseNet), respectively. Experimental results showed that compared with visual observation, traditional machine learning and deep learning algorithms had better objectivity. Furthermore, deep learning models all had good classification performance. The highest average correct recognition rate was 92.97%, while that of the traditional method was only 80.93%. Therefore, drug screening methods based on deep learning could assist in screening the optimal treatment drugs, which help clinicians choose appropriate therapy.

Citation Download Citation

Cheng Zhang, Jianhua Wang, Hongxin Lin, Zhenlin Zhan, Lisheng Lin, and Jian Sui "Drug screening methods based on deep learning", Proc. SPIE 12448, 5th Optics Young Scientist Summit (OYSS 2022), 124480V (15 November 2022); https://doi.org/10.1117/12.2638221

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KEYWORDS

Data modeling

Machine learning

Feature extraction

Phosphorus

Image classification

Image segmentation

Laser irradiation

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