Applications and Prospects of Artificial Intelligence in Integrative Oncology: Bridging Traditional Chinese and Western Pharmacology

Artificial intelligence (AI) is revolutionizing integrative oncology by enabling data-driven discovery and mechanistic insights across traditional and modern pharmacology. This Research Topic, “Applications and Prospects of Artificial Intelligence in Integrative Oncology: Bridging Traditional Chinese and Western Pharmacology,” aims to showcase advances in machine learning, deep learning, and computational modeling that enhance our understanding of cancer pharmacology. We invite studies applying AI to drug discovery, target identification, multi-omics integration, and personalized treatment optimization, particularly within the context of Traditional Chinese Medicine (TCM) and Western medicine integration. Submissions may include algorithm development, model interpretability, large-scale data mining, and predictive modeling of therapeutic efficacy and safety. To ensure translational relevance, we particularly encourage rigorous model evaluation, preferably via temporal (“time‑machine”) testing (training on earlier data and testing on later data) and/or external validation on independent cohorts or datasets, together with transparent reporting of baselines, data leakage controls, calibration, uncertainty, and interpretability. By fostering interdisciplinary collaboration, this collection seeks to accelerate innovation in AI-driven pharmacological research and promote the convergence of computational intelligence and integrative medicine for more precise and effective cancer therapies.The goal of this Research Topic is to advance the application of artificial intelligence (AI) and machine learning techniques in integrative oncology, fostering a deeper understanding of cancer pharmacology through computational innovation. By bridging Traditional Chinese Medicine (TCM) and Western pharmacology, this collection seeks to identify new therapeutic targets, optimize drug combinations, and predict treatment responses using multi-omics and clinical data. We aim to gather interdisciplinary contributions that leverage algorithmic modeling, data-driven analysis, and intelligent systems to unravel complex pharmacological interactions. Ultimately, this Topic intends to promote AI-guided strategies for precision oncology and encourage collaboration between pharmacologists, data scientists, and clinicians to accelerate translational progress in integrative cancer therapy.This Research Topic invites high-quality contributions that apply artificial intelligence (AI) and computational approaches to integrative oncology, emphasizing both methodological innovation and translational application. To ensure practical utility, we encourage manuscripts with rigorous evaluation—preferably temporal (“time‑machine”) validation (train on earlier data, test on later data) and/or external validation on independent cohorts or datasets—and transparent reporting of baselines, data leakage controls, calibration, uncertainty, and interpretability. We welcome Original Research, Reviews, Mini Reviews, and Perspectives covering, but not limited to, the following themes:1. AI-driven drug repurposing and discovery in oncology, including prediction of novel anti-cancer compounds and optimization of therapeutic strategies. 2. Multi-omics and real-world data integration, leveraging genomic, transcriptomic, and clinical datasets to elucidate mechanisms of tumor pharmacology. 3. Mechanism-based modeling of TCM formulas and natural products, using deep learning, systems biology, or quantitative simulations. 4. Prediction of drug–herb interactions and synergistic effects, employing computational pharmacology and safety assessment frameworks.5. Applications of knowledge graphs and large language models (LLMs) for intelligent data mining, mechanistic inference, and hypothesis generation in integrative pharmacology. This Topic encourages interdisciplinary collaboration across pharmacology, oncology, AI, and Traditional Chinese Medicine to advance precision-oriented cancer therapy. Topic editors: Mengyuan Li, Md. Nazim Uddin Source: https://www.frontiersin.org/research-topics/77480/applications-and-prospects-of-artificial-intelligence-in-integrative-oncology-bridging-traditional-chinese-and-western-pharmacology