Estimation of melting temperature of molecular cocrystals using artificial neural network model

Rama Krishna Gamidi; Åke C. Rasmuson

doi:10.1021/acs.cgd.6b01403

Estimation of melting temperature of molecular cocrystals using artificial neural network model

Rama Krishna Gamidi
, Åke C. Rasmuson

Retired Staff

University of Limerick

Research output: Contribution to journal › Article › peer-review

Abstract

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%.

Original language	English
Pages (from-to)	175-182
Number of pages	8
Journal	Crystal Growth and Design
Volume	17
Issue number	1
DOIs	https://doi.org/10.1021/acs.cgd.6b01403
Publication status	Published - 4 Jan 2017

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title = "Estimation of melting temperature of molecular cocrystals using artificial neural network model",

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\%.",

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AU - Rasmuson, Åke C.

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N2 - 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%.

AB - 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%.

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Estimation of melting temperature of molecular cocrystals using artificial neural network model

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