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<p>Advanced algorithms have significantly improved the efficiency of in vitro screening for protein-interactive compounds. However, target antigen (TAA/TSA)-based drug discovery remains challenging, as predictions of compound-protein interaction (CPI) based solely on molecular structure fail to fully elucidate the underlying mechanisms. In this study, we utilized deep learning, specifically TransformerCPI to screen active molecules from a Chinese herb compound library based on protein sequences. Two natural products, Polyphyllin V and Polyphyllin H, were identified as targeting the pan-cancer marker CD133. Their anti-tumor efficacy and safety were confirmed across validation in cancer cell lines, tumor patient-derived organoids, and animal models. Despite their analogous structures and binding affinity to CD133, Polyphyllin V suppresses the PI3K-AKT pathway, inducing pyroptosis and blockage of mitophagy, whereas Polyphyllin H inhibits the Wnt/β-catenin pathway and triggers apoptosis. These distinct mechanisms underscore the potential of combining AI-driven screening with biological validation. This AI-to-patient pipeline identifies Polyphyllin V and Polyphyllin H as CD133-targeted drugs for pan-cancer therapy, and reveals the limitations of virtual screening alone and emphasizes the necessity of live model evaluation in AI-based therapeutic discovery.</p>

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AI-identified CD133-targeting natural compounds demonstrate differential anti-tumor effects and mechanisms in pan-cancer models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月2日

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  • Yibo Hou,
  • Zixian Wang,
  • Wenlin Wang,
  • Qing Tang,
  • Yongde Cai,
  • Siyang Yu,
  • Jin Wang,
  • Xiu Yan,
  • Guocai Wang,
  • Peter E Lobie,
  • Yubo Zhang,
  • Xiaoyong Dai,
  • Shaohua Ma

摘要

Advanced algorithms have significantly improved the efficiency of in vitro screening for protein-interactive compounds. However, target antigen (TAA/TSA)-based drug discovery remains challenging, as predictions of compound-protein interaction (CPI) based solely on molecular structure fail to fully elucidate the underlying mechanisms. In this study, we utilized deep learning, specifically TransformerCPI to screen active molecules from a Chinese herb compound library based on protein sequences. Two natural products, Polyphyllin V and Polyphyllin H, were identified as targeting the pan-cancer marker CD133. Their anti-tumor efficacy and safety were confirmed across validation in cancer cell lines, tumor patient-derived organoids, and animal models. Despite their analogous structures and binding affinity to CD133, Polyphyllin V suppresses the PI3K-AKT pathway, inducing pyroptosis and blockage of mitophagy, whereas Polyphyllin H inhibits the Wnt/β-catenin pathway and triggers apoptosis. These distinct mechanisms underscore the potential of combining AI-driven screening with biological validation. This AI-to-patient pipeline identifies Polyphyllin V and Polyphyllin H as CD133-targeted drugs for pan-cancer therapy, and reveals the limitations of virtual screening alone and emphasizes the necessity of live model evaluation in AI-based therapeutic discovery.