Artificial Intelligence–Driven Identification of Multidrug Resistance Genes in Cancer Genomics

Multidrug resistance (MDR) remains one of the principal causes of therapeutic failure in cancer treatment, limiting the long-term effectiveness of chemotherapy, targeted therapy, and immunotherapy. Recent advances in cancer genomics and artificial intelligence (AI) have created new opportunities for identifying resistance-associated genes and molecular pathways with greater precision and speed. This study examines the application of AI-driven computational frameworks for the detection and interpretation of multidrug resistance genes across heterogeneous cancer genomic datasets. By integrating machine learning, deep learning, and bioinformatics techniques with transcriptomic, epigenomic, and mutational profiles, AI models can uncover hidden patterns linked to treatment resistance and tumor adaptation. The paper discusses major AI algorithms used in resistance prediction, including support vector machines, random forests, convolutional neural networks, and graph-based learning approaches. It further evaluates the contribution of large-scale genomic repositories and multi-omics integration in improving predictive performance and biological interpretability. Key resistance-associated genes frequently identified through AI-assisted analysis include ATP-binding cassette transporters, DNA repair regulators, apoptosis-related genes, and signaling pathway mediators involved in tumor survival and drug efflux. Challenges associated with data imbalance, model interpretability, computational bias, and clinical validation are also critically examined. The study concludes that AI-driven genomic analysis has significant potential to enhance precision oncology by enabling early identification of resistant tumor phenotypes, supporting personalized therapeutic strategies, and accelerating biomarker discovery for improved cancer management.