为了促进纳米医学的发展，深入理解纳米颗粒（NPs）与体内肿瘤的复杂相互作用及其对NPs输送效率的主导作用非常关键。在此，我们提出了一个可解释的XGBoost-SHAP模型，将NPs的物理化学性质和肿瘤基因组特征结合起来，以预测其输送效率。在测试集上，最大输送效率、注射后24小时和168小时的输送效率的相关系数分别为0.66、0.75和0.54。特征重要性分析表明，肿瘤基因组突变及其与NPs性质的相互作用在NPs输送中起着重要作用。通过基因本体富集分析进一步探索了与NPs输送相关的基因的生物途径。我们的工作提供了一个预测和解释对异质性肿瘤的NPs输送效率的流程，并强调同时使用组学数据和可解释的机器学习算法来发现NPs与个体肿瘤之间相互作用的力量，这对于个性化精准纳米医学的发展至关重要。

Understanding the complex interaction between nanoparticles (NPs) and tumors in vivo and how it dominates the delivery efficiency of NPs is critical for the translation of nanomedicine. Herein, we proposed an interpretable XGBoost-SHAP model by integrating the information on NPs physicochemical properties and tumor genomic profile to predict the delivery efficiency. The correlation coefficients were 0.66, 0.75, and 0.54 for the prediction of maximum delivery efficiency, delivery efficiency at 24 and 168 h postinjection for test sets. The analysis of the feature importance revealed that the tumor genomic mutations and their interaction with NPs properties played important roles in the delivery of NPs. The biological pathways of the NP-delivery-related genes were further explored through gene ontology enrichment analysis. Our work provides a pipeline to predict and explain the delivery efficiency of NPs to heterogeneous tumors and highlights the power of simultaneously using omics data and interpretable machine learning algorithms for discovering interactions between NPs and individual tumors, which is important for the development of personalized precision nanomedicine.