Machine-learning predictions of caffeine co-crystal formation accompanying experimental and molecular validations

Caffeine co-crystal formation with other compounds is investigated in this study using a variety of machine learning (ML) methods. A total of 140 caffeine co-crystal data points are used to train classification learners using MATLAB ML models. Kernel neural network, ensemble tree-based, logistic regression, and support vector machine algorithms were among the ML models tested. The logistic regression algorithm produced the most accurate predictions of caffeine-co-crystal formation, with a validation accuracy of 97.1%. Experiments and molecular interaction studies between caffeine and other tea compounds (catechin and catechol) are used to validate ML predictions. As part of the evaluation, a random forest classifier was applied to select 1440 known molecular descriptors, among them 30 descriptors identified as responsible for caffeine co-crystal formation were used for training and validation purpose. The reliability of the trained logistic regression algorithm means that it is suitable for use in predicting possible co-crystals between caffeine and other compounds, thereby providing an understanding of caffeine co-crystals formation without recourse to rigorous experimental tests. Practical applicationsCaffeine co-crystals formation with other tea components is crucial to understand the generation of tea cream, which is undesirable to customers. Using a data-driven approach (or machine learning) to identify the possible molecular combinations involved in co-crystal formation can significantly reduce experimental test requirements. Machine learning data can help with investigations aimed at detailed characterization of ready-to-drink concentrated tea formulations. Furthermore, this study will assist researchers and policymakers in meeting the Sustainable Development Goals of "Industry, Innovation, and Infrastructure. "