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- W2558300894 abstract "A quantitative structure–activity relationship model has been constructed by artificial neural networks for estimation of melting temperature (Tm) 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 Tm 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%." @default.
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- W2558300894 date "2016-12-13" @default.
- W2558300894 modified "2023-10-14" @default.
- W2558300894 title "Estimation of Melting Temperature of Molecular Cocrystals Using Artificial Neural Network Model" @default.
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- W2558300894 doi "https://doi.org/10.1021/acs.cgd.6b01403" @default.
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