Prediction of hydration enthalpy of low molecular weight organic molecules with machine learning regression based on COSMO-SAC
- Title
- Prediction of hydration enthalpy of low molecular weight organic molecules with machine learning regression based on COSMO-SAC
- Abstract
- COSMO-SAC modeling is a reliable method to determine the activity coefficient of the mixtures and is used to predict low molecular weight organic materials hydration enthalpy. A dataset of 96 organic molecules’ activity coefficients in the different solvents (water, ethanol, methanol, toluene, and benzene) mixtures have been obtained in full range composition with COSMO-SAC. The created database has been merged with the FreeSolv dataset to include the hydration enthalpy of these materials as input of machine learning training besides the Van der Waals diameter, other important molecular descriptive. The support vector regressor, random forest regressor, and gradient boosting decision tree regressor have been used for data training and prediction of hydration enthalpy of the organic and pharmaceutical materials. Variation of training and testing rates is most effective parameter in the prediction of enthalpy of hydration. The random forest regression is the most accurate method in the prediction of the enthalpy of hydration with 1.5 % RMSD with a train: test ratio of 0.25:0.75 between the studied methods
- Scientific Type
- غير معروف
- Journal volume
- vol.6,No.1
- Collaboration type
- مشترك
- Publish Date
- June 25, 2023
- Participated Universities (Publication)
-
Alnoor University
- Scopus status
- In Scopus
- Scopus index year
- 2 023
- Scopus quarter
- 5
- Scopus citation score
- 7.300000191
- Clarivate status
- Not In Clarivate
- Pub. Med. status
- Not In PubMed
- Author (Publication)
- مصطفى همام سامي نوح
- Journal (Publication)
-
Chemical Review and Letters
- Publisher (Publication)
- Iranian Chemical Science and Technologies Association
- ISSN
- 2676-7279
- Country (Publication)
-
Iran
- Country type
- عالمية
- College (Publication)
-
College of Pharmacy
- Departement (Publication)
-
Department of Pharmacy
- Media
-
Academic Paper
