Cocrystal Prediction Using Machine Learning Models and Descriptors

Cocrystals are of much interest in industrial application as well academic research, and screening suitable coformers for active pharmaceutical ingredients is the most crucial challenging step cocrystal development. Recently, machine learning techniques attracting researchers many fields including research such quantitative structure-activity/property relationship. In this paper, we develop models to predict formation. We extract descriptor values from simplified molecular-input line-entry system (SMILES) compounds compare by experiments with our collected data 1476 instances. As a result, found that artificial neural network shows great potential it has best accuracy, sensitivity, F1 score. also model achieved comparable performance about half descriptors chosen feature selection algorithms. believe will contribute faster more accurate