Estimation of Melting Temperature of Molecular Cocrystals Using Artificial Neural Network Model

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• 作者：Rama Krishna GamidiÅke. C. Rasmuson
• 刊名：Crystal Growth & Design
• 出版年：2017
• 出版时间：January 4, 2017
• 年：2017
• 卷：17
• 期：1
• 页码：175-182
• 全文大小：498K
• ISSN：1528-7505

文摘

A quantitative structure–activity relationship model has been constructed by artificial neural networks for estimation of melting temperature (T_m) of molecular cocrystals (CCs). On the basis of a literature analysis using SciFinder and Cambridge Structural Database softwares, a database has been created of CCs for four active pharmaceutical ingredients, namely, caffeine, theophylline (THP), nicotinamide (NA), and isonicotinamide (INA). In total, of 61 CCs were included: 14-CAF, 9-THP, 29-INA, and 9-NA. A good correlation was obtained with ANNs to quantify the T_m of the CCs with respect to various coformers. The training process was completed with an average relative error of 2.38%, whereas the relative error for the validation set was 2.89%.