Investigating Spatial Charge Descriptors for Prediction of Cocrystal Formation Using Machine Learning Algorithms

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 sigma-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.