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Individual patient's resistance is exacerbated by the intricate heterogeneity of tumor cells, presenting significant obstacles to effective treatment. To address this challenge, DrugFormer, a novel graph\u2010augmented large language model designed to predict drug resistance at single\u2010cell level is proposed. DrugFormer integrates both serialized gene tokens and gene\u2010based knowledge graphs for the accurate predictions of drug response. After training on comprehensive single\u2010cell data with drug response information, DrugFormer model presents outperformance, with higher F1, precision, and recall in predicting drug response. Based on the scRNA\u2010seq data from refractory multiple myeloma (MM) and acute myeloid leukemia (AML) patients, DrugFormer demonstrates high efficacy in identifying resistant cells and uncovering underlying molecular mechanisms. 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