A mini-review on the application of machine learning in polymer nanogels for drug delivery

被引：16

作者：

Adekoya, Oluwasegun Chijioke ^{[1
,4
]}

Yibowei, Moses Ebiowei ^{[5
]}

Adekoya, Gbolahan Joseph ^{[1
,4
]}

Sadiku, Emmanuel Rotimi ^{[1
]}

Hamam, Yskandar ^{[2
,3
]}

Ray, Suprakas Sinha ^{[4
]}

机构：

[1] Tshwane Univ Technol, Fac Engn & Built Environm, Inst NanoEngn Res INER, Dept Chem Met & Mat Engn, Pretoria, South Africa

[2] Tshwane Univ Technol, French South African Inst Technol FSATI, Dept Elect Engn, ZA-0001 Pretoria, South Africa

[3] Ecole Super Ingenieurs Electrotech & Elect, Cite Descartes,2 Blvd Blaise Pascal, F-93160 Noisy Le Grand, France

[4] CSIR, Natl Ctr Nanostruct Mat, Pretoria, South Africa

[5] Yaba Coll Technol, Dept Polymer & Text Technol, Yaba, Lagos State, Nigeria

来源：

MATERIALS TODAY-PROCEEDINGS | 2022年 / 62卷

基金：

新加坡国家研究基金会;

关键词：

Machine learning; Nanogels; Polymers; Hydrogel; ANN; Drug; CARRIERS;

D O I：

10.1016/j.matpr.2022.02.101

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The advent of nanotechnology has resulted in an exponential improvement in drug delivery systems. Special attention is drawn to the use of nanogels which are nanosized hydrogels as effective drug delivery polymeric materials. Nanogels are 3-dimensional polymeric chains with sizes ranging from 100 to 200 nm. Their non-toxicity, biocompatibility, and biodegradability make them well suited for this purpose. Emerging studies have shown that the use of machine learning (ML) can optimize the drug-carrying and delivery of nanogels. This review would identify the mechanisms of nanogel drug delivery, commonly used machine learning models, areas of possible application of machine learning as it concerns nanogel drug delivery, and limitations in the application of machine learning. (C) 2021 The Authors. Published by Elsevier Ltd.

引用

页码：S141 / S144

页数：4

共 37 条

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