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Estimation of melting temperature of molecular cocrystals using artificial neural network model
University of Limerick.ORCID iD: 0000-0003-1790-2310
2017 (English)In: Crystal Growth & Design, ISSN 1528-7483, E-ISSN 1528-7505, Vol. 17, no 1, p. 175-182Article in journal (Refereed) Published
Abstract [en]

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%.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2017. Vol. 17, no 1, p. 175-182
National Category
Chemical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-260574DOI: 10.1021/acs.cgd.6b01403ISI: 000391417300024Scopus ID: 2-s2.0-85029679467OAI: oai:DiVA.org:kth-260574DiVA, id: diva2:1355998
Note

Cited By :7; Export Date: 30 September 2019; Article

QC 20191024

Available from: 2019-09-30 Created: 2019-09-30 Last updated: 2019-10-24Bibliographically approved

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Rasmuson, Åke Christoffer
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