纳米颗粒
分布(数学)
计算机科学
人工智能
纳米技术
材料科学
数学
数学分析
作者
Kun Mi,Wei-Chun Chou,Qiran Chen,Long Yuan,V. Kamineni,Yashas Kuchimanchi,Chunla He,Nancy A. Monteiro‐Riviere,Jim E. Riviere,Zhoumeng Lin
标识
DOI:10.1016/j.jconrel.2024.08.015
摘要
Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low delivery efficiency (DE) to the tumor site. Understanding the impact of NPs' physicochemical properties on target tissue distribution and tumor DE can help improve the design of nanomedicines. Multiple machine learning and artificial intelligence models, including linear regression, support vector machine, random forest, gradient boosting, and deep neural networks (DNN), were trained and validated to predict tissue distribution and tumor delivery based on NPs' physicochemical properties and tumor therapeutic strategies with the dataset from Nano-Tumor Database. Compared to other machine learning models, the DNN model had superior predictions of DE to tumors and major tissues. The determination coefficients (R
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