ArticleActa pharmaceutica Sinica. B2026
Deep learning-based discovery of tetrahydrocarbazoles as broad-spectrum antitumor agents and click-activated strategy for targeted cancer therapy.
Article in Acta pharmaceutica Sinica. B, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Tetrahydrocarbazole as a Versatile Scaffold in Drug Discovery: A Cross-Target SAR Analysis and Design Paradigms.Molecules (Basel, Switzerland) · 2026Review
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Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. Herein, we 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
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Registered trials
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