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Jun Li, Yang Li, Tian Xie. Bridging Realms: Artificial Intelligence Integrates Omics, Generative Models, and Traditional Medicine for Anticancer Drug Innovation[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2026.101630
Citation: Jun Li, Yang Li, Tian Xie. Bridging Realms: Artificial Intelligence Integrates Omics, Generative Models, and Traditional Medicine for Anticancer Drug Innovation[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2026.101630
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Bridging Realms: Artificial Intelligence Integrates Omics, Generative Models, and Traditional Medicine for Anticancer Drug Innovation

doi: 10.1016/j.jpha.2026.101630

  • 1. School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China;

  • 2. Zhejiang Provincial Key Laboratory of Anti-Cancer Chinese Medicines and Natural Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou, 311121, China;

  • 3. Traditional Chinese Medicine for Reproductive Health Key Laboratory of Zhejiang Province, Hangzhou, 310000, China;

  • 4. Department of Gynecologic Oncology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China

Funds:

The authors gratefully acknowledge the financial support from the Key R&

D Program of Zhejiang (Grant No.: 2025C02198), Zhejiang Provincial Traditional Chinese Medicine Science and Technology Plan (Project No.: 2026ZF74) and Zhejiang Provincial Administration of Traditional Chinese Medicine Co-construction Science and Technology Plan Project (Project No.: GZY-ZJ-KJ-24083).

  • Received Date: Jul. 14, 2025
  • Accepted Date: Apr. 06, 2026
  • Rev Recd Date: Apr. 05, 2026
  • Available Online: Apr. 09, 2026
    Abstract
  • Artificial intelligence (AI) is transforming anti-tumor drug discovery by addressing key challenges across the development pipeline. It enables multimodal data integration (genomics, proteomics, and imaging) through advanced frameworks like integrated graph convolutional network (IGCN) and CrossAttOmics, enhancing target identification and biomarker discovery. Generative drug design using generative adversarial networks (GANs), diffusion models, and variational autoencoders accelerates the creation of novel molecules with optimized properties, such as human carboxylesterase 2A (hCES2A) inhibitors to mitigate irinotecan toxicity. AI-driven prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) leverages graph neural networks and organ-on-chip technologies to improve pharmacokinetic profiling and safety assessment. An emerging area is the integration of traditional Chinese medicine (TCM) with AI. Network pharmacology and machine learning (ML) elucidate multi-target mechanisms of TCM compounds (e.g., β-elemene inducing ferroptosis via ferritin heavy chain 1/glutathione peroxidase 4 (FTH1/GPX4) axis). Bayesian optimization and nanoformulations enhance TCM bioavailability. In clinical translation, AI optimizes trials through radiomics for programmed cell death protein 1 (PD-1) response prediction, circulating tumor DNA (ctDNA) analysis for relapse monitoring, and digital pathology. It also addresses drug resistance via single-cell RNA sequencing (scRNA-seq) to identify resistant subclones and TCM agents. Challenges include data heterogeneity, model interpretability, and clinical validation. Future directions focus on interdisciplinary strategies combining quantum computing for molecular simulations, federated learning for data privacy, and AI-personalized TCM formulations. A 2030 roadmap prioritizes building unified TCM-AI databases and target-specific generative algorithms to bridge empirical knowledge with precision oncology.
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