Abstract
The rise of single-cell sequencing has revolutionized the exploration of drug resistance, revealing the crucial role of cellular heterogeneity in advancing precision medicine. By building computational models from existing single-cell drug response data, we can rapidly annotate cellular responses to drugs. To this end, we developed scGSDR, integrating two computational pipelines grounded in the knowledge of cellular states and gene signaling pathways, both essential for understanding biological gene semantics. scGSDR enhances predictive performance by incorporating gene semantics and employs an interpretability module to identify pathways contributing to drug resistance phenotypes. Extensive validation demonstrates scGSDR's superior predictive accuracy when trained with bulk RNA-seq or scRNA-seq data. The model's application has extended from single-drug predictions to scenarios involving drug combinations. Leveraging pathways of known drug target genes, we found that scGSDR's cell-pathway attention scores are biologically interpretable, which helped us identify potential drug-related genes. Literature review of top-ranking genes in predictions such as BCL2, CCND1, and PIK3CA for PLX4720 confirmed their relevance. Overall, scGSDR, by incorporating gene semantics, enhances predictive modeling of cellular responses to diverse drugs, proving invaluable for scenarios involving both single drug and combination therapies and effectively identifying key resistance-related pathways, thus advancing precision medicine and targeted therapy development.