Ge, Kai published the artcileNovel Computational Approach by Combining Machine Learning with Molecular Thermodynamics for Predicting Drug Solubility in Solvents, Formula: C18H19Cl2NO4, the main research area is antipyrine benzamide doxofylline gefitinib thermodn machine learning.
In this work, a novel strategy that combined mol. thermodn. and machine learning was proposed to accurately predict the solubility of drugs in various solvents. The strategy was based on 16 mol. descriptors representing drug-drug interactions and drug-solvent interactions including phys. parameters, pure perturbed-chain statistical associating fluid theory (PC-SAFT) parameters of drugs and solvents, and mixing rules. These mol. 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 mol. 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.
Industrial & Engineering Chemistry Research published new progress about Drug discovery. 72509-76-3 belongs to class pyridine-derivatives, name is 3-Ethyl 5-methyl 4-(2,3-dichlorophenyl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate, and the molecular formula is C18H19Cl2NO4, Formula: C18H19Cl2NO4.