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Deep learning for in vitro prediction of pharmaceutical formulations
Yang Y.; Ye Z.; Su Y.; Zhao Q.; Li X.; Ouyang D.
Source PublicationActa Pharmaceutica Sinica B
ISSN22113843 22113835

Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of the present research is to apply deep learning methods to predict pharmaceutical formulations. In this paper, two types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assess the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. Results showed that the accuracies of both two deep neural networks were above 80% and higher than other machine learning models; the latter showed good prediction of pharmaceutical formulations. In summary, deep learning employing an automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was developed for the prediction of pharmaceutical formulations for the first time. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical research from experience-dependent studies to data-driven methodologies.

KeywordAutomatic Dataset Selection Algorithm Deep Learning Oral Fast Disintegrating Films Oral Sustained Release Matrix Tablets Pharmaceutical Formulation Small Data
URLView the original
Indexed BySCIE
WOS Research AreaPharmacology & Pharmacy
WOS SubjectPharmacology & Pharmacy
WOS IDWOS:000457201700017
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Cited Times [WOS]:37   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
AffiliationUniversidade de Macau
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Yang Y.,Ye Z.,Su Y.,et al. Deep learning for in vitro prediction of pharmaceutical formulations[J]. Acta Pharmaceutica Sinica B,2019,9(1):177-185.
APA Yang Y.,Ye Z.,Su Y.,Zhao Q.,Li X.,&Ouyang D..(2019).Deep learning for in vitro prediction of pharmaceutical formulations.Acta Pharmaceutica Sinica B,9(1),177-185.
MLA Yang Y.,et al."Deep learning for in vitro prediction of pharmaceutical formulations".Acta Pharmaceutica Sinica B 9.1(2019):177-185.
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