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Updated: Sep 11, 2025

Solubility of Hydrophobic Compounds in Aqueous Solution Using Combinations of Self-assembling Peptide and Amino Acid
Published on: September 20, 2017
Machine learning analysis of pharmaceutical cocrystals solubility parameters in enhancing the drug properties for
Tareq Nafea Alharby1, Bader Huwaimel2,3
1Department of Clinical Pharmacy, College of Pharmacy, University of Ha'il, Ha'il, 81442, Saudi Arabia. tn.alharby@uoh.edu.sa.
Abstract:
A new computational framework based on machine learning was developed for prediction of Hansen solubility parameters in preparation of pharmaceutical cocrystals with improved properties. The models of Kernel Ridge Regression (KRR), Multi-Linear Regression (MLR), and Orthogonal Matching Pursuit (OMP) were optimized in prediction of three Hansen solubility parameters. Each model's performance was assessed via measuring Root Mean Square Error (RMSE), R2, Mean Absolute Error (MAE), and Monte Carlo Cross-Validation (CV) scores using a Tabu Search method for optimization. The results demonstrated that KRR outperformed other models for predicting solubility parameters in the formulation. This comparative evaluation offers valuable perspectives on selecting models for similar regression assignments, stressing the significance of choosing the right algorithm according to particular output demands. The results are useful for design of medicines and screening coformers with solubility enhancement in pharmaceutical co-crystallization.
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