通过DualNet以机器学习驱动的药物固体形式的发现:可靠的盐和共晶的预测和排名
Mohammad Amin Ghanavati1, Bahareh Khalili2, Dino Alberico2
1Chemical and Biochemical Engineering, Western University, London, Ontario N6A 5B9, Canada.
International journal of pharmaceutics
|September 4, 2025
概括
使用新的 DualNet Ensemble 算法加速预测制药盐和联合晶体的形成. 这种机器学习模型有效选多组件固体形式,提高药物开发效率.
科学领域:
- 制药固态化学
- 计算机化药物设计
- 材料科学
背景情况:
- 实验性查药物盐和晶是耗时且低效的.
- 调整药物的固态特性对于配方和生物可用性至关重要.
- 预测模型可以简化最佳多组件形式的识别.
研究的目的:
- 开发和验证用于预测制药盐,共晶和物理混合物的机器学习算法.
- 整合分子图嵌入和物理化学描述器以提高预测准确度.
- 估计预测的不确定性和超越传统的经验规则.
主要方法:
- 开发一个多类分类模型的 DualNet Ensemble 算法.
- 在22298个经过实验验证的多组件项目的数据集上进行培训.
- 用于特征表示的分子图嵌入和物理化学描述符的集成.
主要成果:
- 在宏观平均回忆值为0.952和F1得分为0.940的持久测试组中取得了高性能.
- 证明了优异的校准效率 (ECE = 0.0161),超过了 ΔpKa 规则.
- 在多种化合物中表现出强烈的概括性和成功的前性病例研究.
结论:
- 双网组合算法提供了一个强大,可解释和可靠的实验工具,以加速多组件固体选.
- 这种计算方法显著提高了识别所需药物固体形式的效率.
- 该模型的预测能力和不确定性估计为药物开发中的实验努力提供了宝贵的指导.
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