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Analysis and ANN prediction of melting properties and ideal mole fraction solubility of co-crystals

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dc.contributor.author Gamidi, Rama Krishna
dc.contributor.author Rasmuson, Åke C.
dc.date.accessioned 2020-10-20T14:56:52Z
dc.date.issued 2020
dc.identifier.uri http://hdl.handle.net/10344/9352
dc.description peer-reviewed en_US
dc.description.abstract Different Artificial Neural Network models have been developed and examined for prediction of cocrystal properties based on pure component physical properties only. From the molecular weight, melting temperature, melting enthalpy and melting entropy of the pure compounds, the corresponding melting properties of the cocrystals and the cocrystal ideal solubility have been successfully predicted. Notably, no information, whatsoever about the cocrystals are needed, besides the identification of the two compounds from which the cocrystal is formed. In total, thirty co-crystal systems of eight different model components, namely, Theophylline, Piracetam, Gabapentin-lactam, Tegafur, Nicotinamide, Salicylic acid, Syringic acid and 4,4'-Bipyridine with distinct coformer’s has been chosen as the model system’s for the construction of ANN models. In all the cases, 70% of the data points has been used to train the model and the rest were used to test the capability of the model (as a validation set) as selected through a random selection process. The training process was stopped with overall r2 values above 0.986. In particular, the models capture how the coformer structure influences on the targeted physical properties of cocrystals. en_US
dc.language.iso eng en_US
dc.publisher American Chemical Society en_US
dc.relation 12RC2275 en_US
dc.relation.ispartofseries Crystal Growth and Design;20 (9), pp. 5745-5759
dc.relation.uri https://doi.org/10.1021/acs.cgd.0c00182
dc.rights © 2020 ACS This document is the Accepted Manuscript version of a Published Work that appeared in final form in Crystal,Growth and Design, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.cgd.0c00182 en_US
dc.subject melting enthalpy en_US
dc.subject melting entropy en_US
dc.subject melting point en_US
dc.subject mole fraction solubility en_US
dc.subject co-crystals en_US
dc.subject artificial neural networks en_US
dc.subject predictive models en_US
dc.title Analysis and ANN prediction of melting properties and ideal mole fraction solubility of co-crystals en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1021/acs.cgd.0c00182
dc.contributor.sponsor SFI en_US
dc.contributor.sponsor ERC en_US
dc.relation.projectid SB/S2/RJN012/2017 en_US
dc.date.embargoEndDate 2021-07-14
dc.embargo.terms 2021-07-14 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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