التفاصيل البيبلوغرافية
| العنوان: |
Novel Computational Approach by Combining Machine Learning with Molecular Thermodynamics for Predicting Drug Solubility in Solvents |
| المؤلفون: |
Kai Ge (4032272), Yuanhui Ji (1558669) |
| سنة النشر: |
2021 |
| المجموعة: |
Smithsonian Institution: Digital Repository |
| مصطلحات موضوعية: |
Biophysics, Biochemistry, Medicine, Neuroscience, Pharmacology, Biotechnology, Virology, Computational Biology, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, prediction performance, drug solubility, novel strategy, randomized trees, interaction, RF, Novel Computational Approach, model, fluid theory, generalization evaluation, MLR, SVM, ET, findings show, support vector machine, solubility prediction, drug development, water system, ANN |
| الوصف: |
In this work, a novel strategy that combined molecular thermodynamic and machine learning was proposed to accurately predict the solubility of drugs in various solvents. The strategy was based on 16 molecular descriptors representing drug–drug interactions and drug–solvent interactions including physical parameters, pure perturbed-chain statistical associating fluid theory (PC-SAFT) parameters of drugs and solvents, and mixing rules. These molecular descriptors were inputted into five machine learning algorithms [multiple linear regression (MLR), artificial neural network (ANN), random forest (RF), extremely randomized trees (ET), and support vector machine (SVM)] to train the predictive model. A single-hidden-layer neural network was finally determined as the predictive model for predicting the solubility of drugs in various solvents. The drug solubility in the generalization evaluation set has also been successfully predicted, which indicates the good prediction performance of the model. Three directions for improving the model were summarized as adding molecular descriptors of drug–solvent interactions in the water system and drug–drug interactions in the organic solvent system and expanding the dataset to adequately obtain the features of multiple drugs. These findings show that the proposed model has the capability of solubility prediction, which is expected to provide important information for drug development and drug solvent screening. |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
unknown |
| Relation: |
https://figshare.com/articles/journal_contribution/Novel_Computational_Approach_by_Combining_Machine_Learning_with_Molecular_Thermodynamics_for_Predicting_Drug_Solubility_in_Solvents/14806751 |
| DOI: |
10.1021/acs.iecr.1c00998.s001 |
| الاتاحة: |
https://doi.org/10.1021/acs.iecr.1c00998.s001 |
| Rights: |
CC BY-NC 4.0 |
| رقم الانضمام: |
edsbas.9E58A9B2 |
| قاعدة البيانات: |
BASE |