Development of machine learning model and analysis study of drug solubility in supercritical solvent for green technology development

Kostyrin, Evgeny V.; Ponkratov, Vadim V.; Shan, A.S.

doi:10.1016/j.arabjc.2022.104346

Cited by 14 publications

(1 citation statement)

References 28 publications

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“…Despite the acceptance of thermodynamic models for pharmaceutical solubility, these models are not straightforward to develop for a variety of drug substances. The method of machine learning which is data-driven model has indicated greater performance in terms of fitting accuracy for estimating different drugs solubility in supercritical solvents 14 – 16 .…”

Section: Introductionmentioning

confidence: 99%

Computational intelligence modeling of hyoscine drug solubility and solvent density in supercritical processing: gradient boosting, extra trees, and random forest models

Ghazwani

Begum

2023

Sci Rep

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This work presents the results of using tree-based models, including Gradient Boosting, Extra Trees, and Random Forest, to model the solubility of hyoscine drug and solvent density based on pressure and temperature as inputs. The models were trained on a dataset of hyoscine drug with known solubility and density values, optimized with WCA algorithm, and their accuracy was evaluated using R2, MSE, MAPE, and Max Error metrics. The results showed that Gradient Boosting and Extra Trees models had high accuracy, with R2 values above 0.96 and low MAPE and Max Error values for both solubility and density output. The Random Forest model was less accurate than the other two models. These findings demonstrate the effectiveness of tree-based models for predicting the solubility and density of chemical compounds and have potential applications in determination of drug solubility prior to process design by correlation of solubility and density to input parameters including pressure and temperature.

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Section: Introductionmentioning

confidence: 99%