Article ; Online: Machine learning algorithms for prediction of entrapment efficiency in nanomaterials.
2023 Volume 218, Page(s) 133–140
Abstract: Exploitation of machine learning in predicting performance of nanomaterials is a rapidly growing dynamic area of research. For instance, incorporation of therapeutic cargoes into nanovesicles (i.e., entrapment efficiency) is one of the critical ... ...
Abstract | Exploitation of machine learning in predicting performance of nanomaterials is a rapidly growing dynamic area of research. For instance, incorporation of therapeutic cargoes into nanovesicles (i.e., entrapment efficiency) is one of the critical parameters that ensures proper entrapment of drugs in the developed nanosystems. Several factors affect the entrapment efficiency of drugs and thus multiple assessments are required to ensure drug retention, and to reduce cost and time. Supervised machine learning can allow for the construction of algorithms that can mine data available from earlier studies to predict performance of specific types of nanoparticles. Comparative studies that utilize multiple regression algorithms to predict entrapment efficiency in nanomaterials are scarce. Herein, we report on a detailed methodology for prediction of entrapment efficiency in nanomaterials (e.g., niosomes) using different regression algorithms (i.e., CatBoost, linear regression, support vector regression and artificial neural network) to select the model that demonstrates the best performance for estimation of entrapment efficiency. The study concluded that CatBoost algorithm demonstrated the best performance with maximum R |
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MeSH term(s) | Liposomes ; Machine Learning ; Nanostructures ; Algorithms ; Lipids |
Chemical Substances | Liposomes ; Lipids |
Language | English |
Publishing date | 2023-08-16 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 1066584-5 |
ISSN | 1095-9130 ; 1046-2023 |
ISSN (online) | 1095-9130 |
ISSN | 1046-2023 |
DOI | 10.1016/j.ymeth.2023.08.008 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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