Apply Shennong framework to the pan-cancer landscape and explored the response of the tumor cells to pharmacologic compounds.

Shennong (https://github.com/PeijingZhang/Shennong/) is a deep learning framework for in silico screening of anticancer drugs for targeting each of the landscape cell clusters. Utilizing Shennong, we could predict individual cell responses to pharmacologic compounds, evaluate drug candidates’ tissue damaging effects, and investigate their corresponding action mechanisms. Prioritized compounds in Shennong’s prediction results include FDA-approved drugs currently undergoing clinical trials for new indications, as well as drug candidates reporting anti-tumor activity. Furthermore, the tissue damaging effect prediction aligns with documented injuries and terminated discovery events. This robust and explainable framework has the potential to accelerate the drug discovery process and enhance the accuracy and efficiency of drug screening.

Processed count matrices and cell annotations are available at figshare: https://doi.org/10.6084/m9.figshare.25497445.

Citation: Peijing Zhang†, Xueyi Wang†, Xufeng Cen†, Qi Zhang†, Yuting Fu, Yuqing Mei, Xinru Wang, Renying Wang, Jingjing Wang, Hongwei Ouyang, Tingbo Liang*, Hongguang Xia*, Xiaoping Han*, and Guoji Guo*. A deep learning framework for in silico screening of anticancer drugs at the single-cell level. National Science Review, 2025, 12(2):nwae451. DOI: https://doi.org/10.1093/nsr/nwae451.