Towards machine learning for hydrogel drug delivery systems

被引：24

作者：

Owh, Cally ^{[1
,2
]}

Ho, Dean ^{[2
,3
,4
,5
]}

Loh, Xian Jun ^{[1
,6
,7
]}

Xue, Kun ^{[1
]}

机构：

[1] ASTAR, Inst Mat Res & Engn IMRE, 2 Fusionopolis Way,08-03 Innovis, Singapore 138634, Singapore

[2] Natl Univ Singapore, Dept Biomed Engn, 4 Engn Dr 3,Engn Block 4, Singapore 117583, Singapore

[3] Natl Univ Singapore NUS, N 1 Inst Hlth N 1, 28 Med Dr, Singapore 116456, Singapore

[4] Natl Univ Singapore NUS, Inst Digital Med WisDM, Yong Loo Lin Sch Med, 28 Med Dr, Singapore 116456, Singapore

[5] Natl Univ Singapore NUS, Yong Loo Lin Sch Med, Dept Pharmacol, 16 Med Dr, Singapore 117600, Singapore

[6] Natl Univ Singapore, Dept Mat Sci & Engn, 9 Engn Dr 1, Singapore 117575, Singapore

[7] Nanyang Technol Univ, Sch Mat Sci & Engn, 50 Nanyang Ave,01-30 Gen Off,Block 4-1, Singapore 639798, Singapore

来源：

TRENDS IN BIOTECHNOLOGY | 2023年 / 41卷 / 04期

关键词：

723.4 Artificial Intelligence - 801.3 Colloid Chemistry - 804 Chemical Products Generally;

D O I：

10.1016/j.tibtech.2022.09.019

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

Hydrogel drug delivery system development is complex and laborious, and machine learning (ML) techniques hold great promise in accelerating the process. We highlight recent advances and strategies for data collection and ML, and we discuss the potential for and barriers to the broader use of ML for hydrogel drug delivery systems.

引用

页码：476 / 479

页数：4

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