{"title":"利用机器学习方法通过绿色纳米化过程确定纳米粒子的溶解度:计算建模与优化","authors":"Ahmad J. Obaidullah , Abdulrahman A. Almehizia","doi":"10.1016/j.asej.2024.102946","DOIUrl":null,"url":null,"abstract":"<div><p>The major aim of the current study is to develop a data-driven methodology based on green processing for estimation of drug solubility in supercritical carbon dioxide as the solvent. Several machine learning algorithms were utilized to simulate Capecitabine solubility in supercritical carbon dioxide for green pharmaceutical manufacturing applications which can enhance the solubility of drugs by this method of processing. In the models, the inputs are pressure (P) and temperature (T), and the target output (Y) is solubility. Tree-based ensemble models of RF (Random Forest), ET (Extra Tree), and GB (Gradient Boosting) were selected for modeling in this research in combination with the optimizer to model the process. The hyper-parameters of models were optimized to reduce the error in the fitting. The coefficient of determination (R<sup>2</sup> score) values obtained more than 0.96 and RMSE (root mean square error) for ET, GB, and RF models are 2.91, 2.37, and 4.45, respectively. Based on accurate analyses of results Gradient Boosting selected for primary model in this research. The models were able to estimate the drug solubility which can be used to estimate solubility for a wide range, thereby saving time and costs of measurements.</p></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"15 9","pages":"Article 102946"},"PeriodicalIF":6.0000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2090447924003216/pdfft?md5=2337c8c47a3c08c4bcdcf2da6ac93a16&pid=1-s2.0-S2090447924003216-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Determination of nanoparticle solubility through green nanonization process using machine learning approach: Computational modeling and optimization\",\"authors\":\"Ahmad J. Obaidullah , Abdulrahman A. Almehizia\",\"doi\":\"10.1016/j.asej.2024.102946\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The major aim of the current study is to develop a data-driven methodology based on green processing for estimation of drug solubility in supercritical carbon dioxide as the solvent. Several machine learning algorithms were utilized to simulate Capecitabine solubility in supercritical carbon dioxide for green pharmaceutical manufacturing applications which can enhance the solubility of drugs by this method of processing. In the models, the inputs are pressure (P) and temperature (T), and the target output (Y) is solubility. Tree-based ensemble models of RF (Random Forest), ET (Extra Tree), and GB (Gradient Boosting) were selected for modeling in this research in combination with the optimizer to model the process. The hyper-parameters of models were optimized to reduce the error in the fitting. The coefficient of determination (R<sup>2</sup> score) values obtained more than 0.96 and RMSE (root mean square error) for ET, GB, and RF models are 2.91, 2.37, and 4.45, respectively. Based on accurate analyses of results Gradient Boosting selected for primary model in this research. The models were able to estimate the drug solubility which can be used to estimate solubility for a wide range, thereby saving time and costs of measurements.</p></div>\",\"PeriodicalId\":48648,\"journal\":{\"name\":\"Ain Shams Engineering Journal\",\"volume\":\"15 9\",\"pages\":\"Article 102946\"},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2090447924003216/pdfft?md5=2337c8c47a3c08c4bcdcf2da6ac93a16&pid=1-s2.0-S2090447924003216-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ain Shams Engineering Journal\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2090447924003216\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ain Shams Engineering Journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2090447924003216","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
Determination of nanoparticle solubility through green nanonization process using machine learning approach: Computational modeling and optimization
The major aim of the current study is to develop a data-driven methodology based on green processing for estimation of drug solubility in supercritical carbon dioxide as the solvent. Several machine learning algorithms were utilized to simulate Capecitabine solubility in supercritical carbon dioxide for green pharmaceutical manufacturing applications which can enhance the solubility of drugs by this method of processing. In the models, the inputs are pressure (P) and temperature (T), and the target output (Y) is solubility. Tree-based ensemble models of RF (Random Forest), ET (Extra Tree), and GB (Gradient Boosting) were selected for modeling in this research in combination with the optimizer to model the process. The hyper-parameters of models were optimized to reduce the error in the fitting. The coefficient of determination (R2 score) values obtained more than 0.96 and RMSE (root mean square error) for ET, GB, and RF models are 2.91, 2.37, and 4.45, respectively. Based on accurate analyses of results Gradient Boosting selected for primary model in this research. The models were able to estimate the drug solubility which can be used to estimate solubility for a wide range, thereby saving time and costs of measurements.
期刊介绍:
in Shams Engineering Journal is an international journal devoted to publication of peer reviewed original high-quality research papers and review papers in both traditional topics and those of emerging science and technology. Areas of both theoretical and fundamental interest as well as those concerning industrial applications, emerging instrumental techniques and those which have some practical application to an aspect of human endeavor, such as the preservation of the environment, health, waste disposal are welcome. The overall focus is on original and rigorous scientific research results which have generic significance.
Ain Shams Engineering Journal focuses upon aspects of mechanical engineering, electrical engineering, civil engineering, chemical engineering, petroleum engineering, environmental engineering, architectural and urban planning engineering. Papers in which knowledge from other disciplines is integrated with engineering are especially welcome like nanotechnology, material sciences, and computational methods as well as applied basic sciences: engineering mathematics, physics and chemistry.