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 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.

Deep learning  /  Phenotypic screening  /  Tetrahydrocarbazoles  /  Drug delivery  /  Click-activated prodrug  /  Antitumor  /  Drug discovery  /  p53