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• W2960097748 endingPage "111369" @default.
• W2960097748 startingPage "111369" @default.
• W2960097748 abstract "General solubility prediction models were developed using both classic least square and a novel method, in order to predict the solubility of the solutes in methanol + water binary solvent system. The novel approach to the regression analysis was investigated using an error minimization method. This aim was achieved by using a user-defined loss function regression, instead of the classic least square regression approach. To examine the results of the novel methodology, previous solubility data of 41 solutes were used for comparison. Both least square and novel methods were applied to the Jouyban-Acree model, Jouyban-Acree model in combination with Abraham parameters, and the modified Wilson model. The generally trained versions of the mentioned models produced more accurate predictions using the novel method than the least square method that has been confirmed by t-test analyses. The Jouyban-Acree model was the most accurate model among other generally trained models. Finally, the results were validated using a cross-validation analysis which produced the acceptable prediction accuracy of 24.6% mean percentage deviation (MPD) for the new methodology against 32.1% of the least square method. Also a new arithmetically transformed version of aforementioned models was introduced in this study to make the calculations easier to execute." @default.
• W2960097748 created "2019-07-23" @default.
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• W2960097748 date "2019-10-01" @default.
• W2960097748 modified "2023-10-17" @default.
• W2960097748 title "A new computational method for drug solubility prediction in methanol + water mixtures" @default.
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• W2960097748 doi "https://doi.org/10.1016/j.molliq.2019.111369" @default.
• W2960097748 hasPublicationYear "2019" @default.
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