Machine learning analysis of pharmaceutical cocrystals solubility parameters in enhancing the drug properties for advanced pharmaceutical manufacturing.

IF 3.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Reports Pub Date : 2025-08-15 DOI:10.1038/s41598-025-12886-8

Tareq Nafea Alharby, Bader Huwaimel

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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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机器学习分析药物共晶溶解度参数在提高药物性能中的应用。

提出了一种新的基于机器学习的计算框架，用于预测制备具有改进性质的药物共晶的Hansen溶解度参数。对核岭回归（KRR）、多元线性回归（MLR）和正交匹配追踪（OMP）模型进行了优化，预测了3个汉森溶解度参数。每个模型的性能通过测量均方根误差（RMSE）、R2、平均绝对误差（MAE）和蒙特卡罗交叉验证（CV）分数来评估，使用禁忌搜索方法进行优化。结果表明，KRR在预测配方中的溶解度参数方面优于其他模型。这种比较评价为为类似的回归任务选择模型提供了有价值的视角，强调了根据特定的输出需求选择正确算法的重要性。研究结果可用于药物设计和药物共结晶中增强溶解度的共晶构象的筛选。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Reports Natural Science Disciplines-

CiteScore

7.50

自引率

4.30%

发文量

19567

审稿时长

3.9 months

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