An advanced computational method for studying drug nanonization using green supercritical-based processing for improvement of pharmaceutical bioavailability in aqueous media
- Title
- An advanced computational method for studying drug nanonization using green supercritical-based processing for improvement of pharmaceutical bioavailability in aqueous media
- Abstract
-
In this study, we implemented and compared various non-mechanistic based models for prediction of drug solubility in supercritical solvent. The data were collected from references and the models were built considering various operational circumstances. Small data sets, like the solubility data used in this study, have always been one of the challenges for modeling in machine learning method. In this study, in order to solve the regression problem related to the solubility of drugs, which includes 32 laboratory data, we implemented and studied models that are naturally compatible with very small data like solubility data of drugs in solvents. These models included Random Forest (RF), KNN and Extra Tree (ET). After obtaining the best settings for each model, their final results were compared in terms of accuracy for predicting drug solubility. The ET model had the best result with a score of 0.9999 on the R2 criterion. Random forests with 0.978 and KNN with 0.972 also had acceptable regression results. Finally, the trained model was used to display and evaluate the effect of input parameters like pressure and temperature on drug solubility to understand the process.
- Scientific Type
- غير معروف
- Journal volume
- vol.381,121805
- Collaboration type
- مشترك
- Publish Date
- July 1, 2023
- Participated Universities (Publication)
-
Alnoor University
- Scopus status
- In Scopus
- Scopus index year
- 2 023
- Scopus quarter
- 1
- Scopus citation score
- 9.699999809
- Clarivate status
- In Clarivate
- Clarivate index year
- 2 023
- Clarivate impafact
- 6.632999897
- Pub. Med. status
- Not In PubMed
- Author (Publication)
-
احمد فيصل مطيع محمد صالح
- Journal (Publication)
-
Journal of Molecular Liquids
- Publisher (Publication)
-
Elsevier
- ISSN
- 0167-7322
- Country (Publication)
-
Netherlands
- Country type
- عالمية
- College (Publication)
-
College of Pharmacy
- Departement (Publication)
-
Department of Pharmacy
- Media
-
Academic Paper
