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. 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 <i>R</i>-(-)-WJ0909 (WJ0909B), demonstrated optimal antitumor efficacy <i>in vitro</i> and <i>ex vivo</i> 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.