Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent.

ABSTRACT: Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO₂ was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility. The data was collected for solubility optimization of Decitabine at the temperature 308-338 K, and pressure 120-400 bar used as the inputs to the machine learning models. A dataset of 32 data points and two inputs (P and T) have been applied to optimize the solubility. The only output is Y = solubility, which is Decitabine mole fraction solubility in the solvent. The developed models are three models including Kernel Ridge Regression (KRR), Decision tree Regression (DTR), and Gaussian

SUBMITTER: Alshahrani SM 

PROVIDER: S-EPMC9640585 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature