https://doi.org/10.1016/j.apsb.2025.10.005
This new article publication from Acta Pharmaceutica Sinica B , discusses the deep learning-based discovery of tetrahydrocarbazoles as broad-spectrum antitumor agents and click-activated strategy for targeted cancer therapy.
Phenotypic screening has played an important role in discovering innovative small-molecule drugs and clinical candidates with unique molecular mechanisms of action. However, conducting cell-based high-throughput screening from vast compound libraries is extremely time-consuming and expensive. Fortunately, deep learning has provided a new paradigm for identifying compounds with specific phenotypic properties. The authors of this article developed a data-driven classification-generation cascade model to discover new chemotype antitumor drugs. Through wet-lab validation, WJ0976 and WJ0909 were identified as tetrahydrocarbazole derivatives and displayed potent broad-spectrum antitumor activity as well as growth inhibitory properties against multidrug-resistant cancer cells. Furthermore, the R -(−)-WJ0909 (WJ0909B), demonstrated optimal antitumor efficacy in vitro and ex vivo patient-derived organoids (PDOs). Further investigations revealed that WJ0909B upregulates p53 expression and cause mitochondria-dependent endogenous apoptosis. Moreover, WJ0909B and the click-activated prodrug WJ0909B-TCO potently inhibited tumor growth in cell-derived xenograft models. This research highlights the significant potential of deep learning-guided approach to phenotypic drug discovery for anticancer drugs and the strategy of click-activated prodrug for targeted cancer therapy.
Keywords: Deep learning, Phenotypic screening, Tetrahydrocarbazoles, Drug delivery, Click-activated prodrug, Antitumor, Drug discovery, p53
Graphical Abstract: available at https://ars.els-cdn.com/content/image/1-s2.0-S2211383525006781-ga1_lrg.jpg
This study established a cascade model by integrating deep learning-driven classifiers and GDL models, identified tetrahydrocarbazole derivatives with subnanomolar activity against pan-cancer cells and patient-derived organoids, and click-activated prodrug strategy showed potent efficacy with minimal toxicity.
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The Journal of the Institute of Materia Medica, the Chinese Academy of Medical Sciences and the Chinese Pharmaceutical Association .
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CiteScore: 24.3
Impact Factor: 14.6 (Top 6 journal in the category of Pharmacology and pharmacy)
JIF without self-citation: 13.8
ISSN 2211-3835
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Xue Liu, Yalan Lu, Qichen Chen, Minjian Yang, Shize Li, Hanyu Sun, Xiangying Liu, Jingjie Yan, Liangning Li, Nan Xiang, Yan Lu, Qi Geng, Yiqiao Deng, Baolian Wang, Jing Jin, Hong Zhao, Xiandao Pan, Ahmed Al-Harrasi, Tingting Du, Wei Song, Xiaojian Wang, Deep learning-based discovery of tetrahydrocarbazoles as broad-spectrum antitumor agents and click-activated strategy for targeted cancer therapy, Acta Pharmaceutica Sinica B, Volume 16, Issue 1, 2026, Pages 406-422, ISSN 2211-3835, https://doi.org/10.1016/j.apsb.2025.10.005