Protocol for vision transformer-based evaluation of drug potency using images processed by an optimized Sobel operator

• Published in: STAR protocols Vol. 4; no. 2; p. 102259
• Main Authors: Wang, Yongheng, Zhang, Weidi, Wu, Yi, Qu, Chuyuan, Hu, Hongru, Lee, Teresa, Lin, Siyu, Zhang, Jiawei, Lam, Kit S., Wang, Aijun
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 16.06.2023; Elsevier
• Subjects: Biotechnology and bioengineering; Cancer; Computer sciences; High Throughput Screening; Microscopy; Protocol; High Throughput Screening; Computer sciences; Biotechnology and bioengineering; Microscopy; Cancer
• ISSN: 2666-1667; 2666-1667
• DOI: 10.1016/j.xpro.2023.102259

Conventional approaches for screening anticancer drugs rely on chemical reactions, which are time consuming, labor intensive, and costly. Here, we present a protocol for label-free and high-throughput assessment of drug efficacy using a vision transformer and a Conv2D. We describe the steps for cell culture, drug treatment, data collection, and preprocessing. We then detail the building of deep learning models and their use to predict drug potency. This protocol can be adapted for screening chemicals that affect the density or morphological features of cells. For complete details on the use and execution of this protocol, please refer to Wang et al.1 [Display omitted] •Protocol for label-free evaluation of drug potency using a vision transformer and a Conv2D•A cost-effective method for high-throughput screening of anticancer drugs•An adaptable method for screening molecules that affect cell density and shape Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Conventional approaches for screening anticancer drugs rely on chemical reactions, which are time consuming, labor intensive, and costly. Here, we present a protocol for label-free and high-throughput assessment of drug efficacy using a vision transformer and a Conv2D. We describe the steps for cell culture, drug treatment, data collection, and preprocessing. We then detail the building of deep learning models and their use to predict drug potency. This protocol can be adapted for screening chemicals that affect the density or morphological features of cells.