纳米医学
计算机科学
过程(计算)
吞吐量
选择(遗传算法)
人工智能
纳米技术
纳米颗粒
材料科学
电信
无线
操作系统
作者
Sean Hamilton,Benjamin R. Kingston
标识
DOI:10.1016/j.copbio.2023.103043
摘要
Achieving specific and targeted delivery of nanomedicines to diseased tissues is a major challenge. This is because the process of designing, formulating, testing, and selecting a nanoparticle delivery vehicle for a specific disease target is governed by complex multivariate interactions. Computational modeling and artificial intelligence are well-suited for analyzing and modeling large multivariate datasets in short periods of time. Computational approaches can be applied to help design nanomedicine formulations, interpret nanoparticle–biological interactions, and create models from high-throughput screening techniques to improve the selection of the ideal nanoparticle carrier. In the future, many steps in the nanomedicine development process will be done computationally, reducing the number of experiments and time needed to select the ideal nanomedicine formulation.
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