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Experimental and Computational Prediction of Glass Transition Temperature of Drugs
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.ORCID iD: 0000-0001-8028-7360
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
2014 (English)In: JOURNAL OF CHEMICAL INFORMATION AND MODELING, ISSN 1549-9596, Vol. 54, no 12, 3396-3403 p.Article in journal (Refereed) Published
Abstract [en]

Glass transition temperature (T-g) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between T-g and melting temperature (T-m) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of T-g were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on T-m predicted T-g with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict T-g of drug-like molecules with high accuracy were developed. If T-m is available, a simple linear regression can be used to predict T-g. However, the results also suggest that support vector regression and calculated molecular descriptors can predict T-g with equal accuracy, already before compound synthesis.

Place, publisher, year, edition, pages
2014. Vol. 54, no 12, 3396-3403 p.
National Category
Computer Science Pharmacology and Toxicology
Identifiers
URN: urn:nbn:se:uu:diva-243056DOI: 10.1021/ci5004834ISI: 000347137500011PubMedID: 25361075OAI: oai:DiVA.org:uu-243056DiVA: diva2:787788
Available from: 2015-02-11 Created: 2015-02-04 Last updated: 2016-12-07

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Alzghoul, AhmadAlhalaweh, AmjadMahlin, DennyBergström, Christel A. S.

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