Solubility of Pharmaceutical Compounds in Organic Solvents Using Artificial Nural Network and Correlation Model

Document Type : Regular Article

Authors

1 Department of Chemistry, Payame Noor University, P. O. Box 19395-3697, Tehran, Iran

2 Department of Chemistry, Eram Institute of Higher Education, P. O. Box 71957-46733, Shiraz, Iran

3 Department of chemistry, Payame Noor University, P.O.Box:19395-3697 Tehran, Iran

10.22036/pcr.2023.391870.2318

Abstract

In their earlier research, the authors explored the correlation of solid-liquid solubility using an updated semi-theoretical equation (Eur. J. Pharm. Sci. 2019, 143, 1-13). This current study applies an Artificial Neural Network (ANN) and a refined Apelblat model to predict the solubility of nine pharmaceutical compounds in pure organic solvents over an extensive temperature range. By training the optimized network using the back-propagation method of the Levenberg-Marquardt algorithm, optimal parameters —such as neurons, hidden layers, and transfer function — were established in the ANN through the utilization of training, testing, and validation data. This network was trained with 764 data points and subsequently tested and validated with 164 data points. A satisfactory correlation of 1.33% was obtained from the enhanced Apelblat model across 1092 data points. The findings from thermodynamic analysis and solubility parameters can optimize the purification process in the synthesis of pharmaceutical compounds.

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Solubility of Pharmaceutical Compounds in Organic Solvents Using Artificial Nural Network and Correlation Model

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Physical Chemistry Research
Volume 12, Issue 3 - Serial Number 43
September 2024
Pages 567-578

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History

  • Receive Date: 04 April 2023
  • Revise Date: 08 November 2023
  • Accept Date: 19 November 2023

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