A comprehensive review recently published in Current Molecular Pharmacology (2026, Volume 19, Pages 85–96) examines the rapidly evolving landscape of computational tools for predicting tumour drug resistance. Led by Jia Wang, Hong‑Rui Zhu, and corresponding authors Zhi‑Chun Gu and Hou‑Wen Lin from Shanghai Jiao Tong University School of Medicine, the article systematically maps how artificial intelligence—particularly machine and deep learning—is being harnessed to integrate multi‑omics data from large‑scale repositories such as TCGA and GDSC. These approaches are helping to decode resistance mechanisms across chemotherapy, targeted therapy, and immunotherapy, while also pointing to novel predictive dimensions such as cancer‑associated thrombosis.
The authors emphasise that standardised databases and sophisticated preprocessing pipelines are now essential for transforming heterogeneous genomic, transcriptomic, and clinical data into reliable model inputs. Yet they caution that data sparsity, batch effects, and the "black‑box" nature of many deep‑learning models remain substantial barriers to clinical adoption. "The inherent trade‑off between model accuracy and interpretability undermines clinician trust and limits real‑world adoption," notes Dr. Gu. To address this, the review advocates for explainable AI frameworks, multimodal fusion strategies, and the integration of dynamic liquid‑biopsy monitoring to capture resistance evolution in real time.
