Investigating Spatial Charge Descriptors for Prediction of Cocrystal Formation Using Machine Learning Algorithms
Investigating Spatial Charge Descriptors for Prediction of Cocrystal Formation Using Machine Learning Algorithms
复制标题
DOI：
10.1021/acs.cgd.2c00812
复制
发表时间：
2022-10
期刊：
影响因子：
--
通讯作者：
Yingquan Hao;Ying-Chieh Hung;Y. Shimoyama
中科院分区：
文献类型：
--
作者：
Yingquan Hao;Ying-Chieh Hung;Y. Shimoyama
近年来，通过共晶进行药物修饰由于其对药物理化性质的调节具有高度的灵活性而引起了人们的极大关注。为了降低筛选实验的成本，机器学习（ML）算法已被证明是快速筛选共晶形成的最有效方法之一。然而，分子描述符的选择对其预测精度有显著影响。在这项工作中，两个空间电荷描述符（COSMO-based σ-profile和三维（3D）空间电荷）被引入ML建模，并开发了两个创新的COSMO-SVM和3D-CNN ML算法来筛选共晶形成。与使用扩展连接指纹（ECFP）和图卷积网络（GCN）的预先提出的ML方法相比，使用空间电荷描述符的两个建议的ML模型表现出上级预测准确性。考虑到计算效率和准确性，3D-CNN将是共晶形成筛选任务中非常有效的ML模型。此外，通过机器学习预测了诺氟沙星两种新共晶的形成，并通过实验获得了共晶。
Recently, drug modification via cocrystals has attracted great attention due to its high flexibility for the modulation of drug physicochemical properties. To reduce the cost of screening experiments, machine learning (ML) algorithms have proven to be one of the most effective ways to rapidly screen cocrystal formation. However, the choice of molecular descriptors has a significant impact on its prediction accuracy. In this work, two space-charge descriptors (COSMO-based σ-profile and three-dimensional (3D) spatial charge) are introduced into ML modeling, and two innovative COSMO-SVM and 3D-CNN ML algorithms are developed to screen the cocrystal formation. The two proposed ML models using space-charge descriptors demonstrate superior predictive accuracy for cocrystal formation compared to preproposed ML methods using extended connectivity fingerprints (ECFPs) and graph convolutional networks (GCNs). Considering computational efficiency and accuracy, 3D-CNN would be a very efficient ML model in the cocrystal formation screening task. In addition, the formation of two new cocrystals of norfloxacin was predicted by machine learning, and the cocrystals were obtained experimentally.