Towards machine learning for hydrogel drug delivery systems
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...
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| Veröffentlicht in: | Trends in biotechnology (Regular ed.) 2023-04, Vol.41 (4), p.476-479 |
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| container_title | Trends in biotechnology (Regular ed.) |
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| creator | Owh, Cally Ho, Dean Loh, Xian Jun Xue, Kun |
| description | 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. |
| doi_str_mv | 10.1016/j.tibtech.2022.09.019 |
| format | Article |
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| fulltext | fulltext |
| identifier | ISSN: 0167-7799 |
| ispartof | Trends in biotechnology (Regular ed.), 2023-04, Vol.41 (4), p.476-479 |
| issn | 0167-7799 1879-3096 |
| language | eng |
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| source | MEDLINE; Elsevier ScienceDirect Journals Complete; ProQuest Central UK/Ireland |
| subjects | Algorithms Artificial Intelligence Biomedical materials Data collection Data mining Datasets Drug delivery Drug delivery systems Fuzzy logic hydrogel Hydrogels Learning algorithms Libraries Machine Learning Neural networks Peptides Polymers Proteins |
| title | Towards machine learning for hydrogel drug delivery systems |
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