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Prediction of Solid State Properties of Cocrystals Using Artificial Neural Network Modeling
University of Limerick.ORCID iD: 0000-0003-1790-2310
2018 (English)In: Crystal Growth & Design, ISSN 1528-7483, Vol. 18, no 1, p. 133-144Article in journal (Refereed) Published
Abstract [en]

Using Artificial Neural Networks (ANNs), four distinct models have been developed for the prediction of solid-state properties of cocrystals: melting point, lattice energy, and crystal density. The models use three input parameters for the pure model compound (MC) and three for the pure coformer. In addition, as an input parameter the model uses the pKa difference between the MC and the coformer, and a 1:1 MC−conformer binding energy as calculated by a force field method. Notably, the models require no data for the actual cocrystals. In total, 61 CCs (two-component molecular cocrystals) were used to construct the models, and melting temperatures and crystal densities were extracted from the literature for four MCs: caffeine, theophylline, nicotinamide, and isonicotinamide. The data set includes 14 caffeine cocrystals, 9 theophylline cocrystals, 9 nicotinamide cocrystals, and 29 isonicotinamide cocrystals. Model-I is trained using known cocrystal melting temperatures, lattice energies, and crystal densities, to predict all three solid-state properties simultaneously. The average relative deviation for the training set is 2.49%, 6.21%, and 1.88% for the melting temperature, lattice energy, and crystal density, respectively, and correspondingly 6.26%, 4.58%, and 0.99% for the valdation set. Model-II, model-III, and model-IV were built using the same input neurons as in model-I, for separate prediction of each respective output solid−state property. For these models the average relative deviation for the training sets becomes 1.93% for the melting temperature model-II, 1.29% for the lattice energy model-III, and 1.03% for the crystal density model-IV, and correspondingly 2.23%, 2.40%, and 1.77% for the respective validation sets.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2018. Vol. 18, no 1, p. 133-144
National Category
Chemical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-260102DOI: 10.1021/acs.cgd.7b00966ISI: 000419749400019Scopus ID: 2-s2.0-85040176259OAI: oai:DiVA.org:kth-260102DiVA, id: diva2:1355949
Note

Cited By :8; Export Date: 25 September 2019

QC 20191029

Available from: 2019-09-30 Created: 2019-09-30 Last updated: 2019-10-29Bibliographically approved

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Rasmuson, Åke Christoffer
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