Cocrystal Prediction Tool (CCPT): A Web Server for Deep Learning-Assisted Cocrystal Screening and Density Evaluation

Cocrystallization, as a molecular modification strategy, has exhibited great success in the chemistry and material fields. To reduce time and labor costs in experiments, machine learning of artificial intelligence (AI) has been introduced to accelerate cocrystal development. Despite the theoretical success of the exploits, their usages suffer from professional programmed operation and substantial human intervention, thus hindering their application in practice. To fill up the gap between theory and practical application, we explore a one-step and user-friendly cocrystal prediction web server (CCPT), which integrates two state-of-the-art deep learning models with high generalization and accuracy, including cocrystal screening and density evaluation. Users only need to upload molecule pair files and select the job type. CCPT can automatically perform feature descriptor calculation and prediction tasks (cocrystallization formation and their densities). All prediction results will be packaged into a file format that can be visualized and downloaded on the web page by users. The entire process under the ergonomic graphical interface is code-free operation and a streamlined workflow with minimum human intervention and thus very simple and convenient for the end-user, especially for experimental investigators. CCPT is free and accessible at http://www.scuccpt.cn. We expect that it will be a useful design tool for diverse cocrystal fields.