dc.contributor.author | Melagraki, G. | en |
dc.contributor.author | Afantitis, Antreas | en |
dc.contributor.author | Sarimveis, H. | en |
dc.contributor.author | Koutentis, Panayiotis Andreas | en |
dc.contributor.author | Kollias, G. | en |
dc.contributor.author | Igglessi-Markopoulou, O. | en |
dc.creator | Melagraki, G. | en |
dc.creator | Afantitis, Antreas | en |
dc.creator | Sarimveis, H. | en |
dc.creator | Koutentis, Panayiotis Andreas | en |
dc.creator | Kollias, G. | en |
dc.creator | Igglessi-Markopoulou, O. | en |
dc.date.accessioned | 2019-11-21T06:21:26Z | |
dc.date.available | 2019-11-21T06:21:26Z | |
dc.date.issued | 2009 | |
dc.identifier.issn | 1381-1991 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/55845 | |
dc.description.abstract | A linear Quantitative Structure-Activity Relationship (QSAR) is developed in this work for modeling and predicting HDAC inhibition by 5-pyridin-2-yl- thiophene-2-hydroxamic acids. In particular, a five-variable model is produced by using the Multiple Linear Regression (MLR) technique and the Elimination Selection-Stepwise Regression Method (ES-SWR) on a database that consists of 58 recently discovered 5-pyridin-2-yl-thiophene-2-hydroxamic acids and 69 descriptors. The physical meaning of the selected descriptors is discussed in detail. The validity of the proposed MLR model is established using the following techniques: cross validation, validation through an external test set and Y-randomization. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined. Based on the produced model, an in silico-screening study explores novel structural patterns and suggests new potent lead compounds. © 2009 Springer Science+Business Media B.V. | en |
dc.source | Molecular diversity | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-68349126938&doi=10.1007%2fs11030-009-9115-2&partnerID=40&md5=a928b5b71b4af9c85f15be39a7c1b9e6 | |
dc.subject | Computer Simulation | en |
dc.subject | article | en |
dc.subject | Algorithms | en |
dc.subject | Regression Analysis | en |
dc.subject | priority journal | en |
dc.subject | Reproducibility of Results | en |
dc.subject | drug activity | en |
dc.subject | drug screening | en |
dc.subject | Enzyme Inhibitors | en |
dc.subject | Pyridines | en |
dc.subject | physical chemistry | en |
dc.subject | Physicochemical Phenomena | en |
dc.subject | IC 50 | en |
dc.subject | Drug Discovery | en |
dc.subject | drug inhibition | en |
dc.subject | QSAR | en |
dc.subject | quantitative structure activity relation | en |
dc.subject | Quantitative Structure-Activity Relationship | en |
dc.subject | drug structure | en |
dc.subject | HDAC | en |
dc.subject | Histone Deacetylase Inhibitors | en |
dc.subject | Models, Chemical | en |
dc.subject | multiple linear regression analysis | en |
dc.subject | Thiophenes | en |
dc.subject | In silico screening | en |
dc.subject | histone deacetylase inhibitor | en |
dc.subject | Histone deacetylases | en |
dc.subject | Hydroxamic acids | en |
dc.title | Predictive QSAR workflow for the in silico identification and screening of novel HDAC inhibitors | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1007/s11030-009-9115-2 | |
dc.description.volume | 13 | |
dc.description.issue | 3 | |
dc.description.startingpage | 301 | |
dc.description.endingpage | 311 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Χημείας / Department of Chemistry | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Cited By :35</p> | en |
dc.source.abbreviation | Mol.Diversity | en |
dc.contributor.orcid | Koutentis, Panayiotis Andreas [0000-0002-4652-7567] | |
dc.contributor.orcid | Afantitis, Antreas [0000-0002-0977-8180] | |
dc.contributor.orcid | Igglessi-Markopoulou, O. [0000-0002-7683-8526] | |
dc.gnosis.orcid | 0000-0002-4652-7567 | |
dc.gnosis.orcid | 0000-0002-0977-8180|0000-0002-7683-8526 | |