# AI Improves Prediction of Cancer Drug Resistance > Source: > Published: 2026-06-27 02:38:05+00:00 A review published in Current Molecular Pharmacology (2026, Volume 19, Pages 85-96) surveys computational approaches for predicting tumour drug resistance, led by Jia Wang, Hong-Rui Zhu, Zhi-Chun Gu, and Hou-Wen Lin of Shanghai Jiao Tong University School of Medicine, according to News-Medical. The article maps how **machine learning** and **deep learning** models integrate multi-omics data from repositories such as **TCGA** and **GDSC** to study resistance across chemotherapy, targeted therapy, and immunotherapy. The authors report that standardized databases and robust preprocessing pipelines are essential for model inputs, while challenges include data sparsity, batch effects, and deep models' black-box nature. News-Medical quotes Dr. Zhi-Chun Gu: "The inherent trade-off between model accuracy and interpretability undermines clinician trust and limits real-world adoption." The review advocates explainable AI, multimodal fusion, longitudinal liquid monitoring, specialised tools for high-risk subgroups such as patients with cancer-associated thrombosis, and calls for unified data standards and prospective clinical validation, News-Medical reports. Professor Hou-Wen Lin is quoted: "Our goal is to move beyond generic predictions and deliver tailored insights for the patients who need them most."