dc.contributor.author | Afantitis, Antreas | en |
dc.contributor.author | Melagraki, G. | en |
dc.contributor.author | Koutentis, Panayiotis Andreas | en |
dc.contributor.author | Sarimveis, H. | en |
dc.contributor.author | Kollias, G. | en |
dc.creator | Afantitis, Antreas | en |
dc.creator | Melagraki, G. | en |
dc.creator | Koutentis, Panayiotis Andreas | en |
dc.creator | Sarimveis, H. | en |
dc.creator | Kollias, G. | en |
dc.date.accessioned | 2019-11-21T06:16:14Z | |
dc.date.available | 2019-11-21T06:16:14Z | |
dc.date.issued | 2011 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/55228 | |
dc.description.abstract | In this work we have developed an in silico model to predict the inhibition of β-amyloid aggregation by small organic molecules. In particular we have explored the inhibitory activity of a series of 62 N-phenylanthranilic acids using Kohonen maps and Counterpropagation Artificial Neural Networks. The effects of various structural modifications on biological activity are investigated and novel structures are designed using the developed in silico model. More specifically a search for optimized pharmacophore patterns by insertions, substitutions, and ring fusions of pharmacophoric substituents of the main building block scaffolds is described. The detection of the domain of applicability defines compounds whose estimations can be accepted with confidence. © 2010 Elsevier Masson SAS. All rights reserved. | en |
dc.source | European journal of medicinal chemistry | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-79151478389&doi=10.1016%2fj.ejmech.2010.11.029&partnerID=40&md5=617674b86f2449b53e3b75d23534b9c1 | |
dc.subject | β-Amyloid inhibitors | el |
dc.subject | article | en |
dc.subject | prediction | en |
dc.subject | Predictive Value of Tests | en |
dc.subject | drug screening | en |
dc.subject | Cluster Analysis | en |
dc.subject | Structure-Activity Relationship | en |
dc.subject | artificial neural network | en |
dc.subject | Neural Networks (Computer) | en |
dc.subject | Ligands | en |
dc.subject | computer model | en |
dc.subject | protein aggregation | en |
dc.subject | Molecular Structure | en |
dc.subject | High-Throughput Screening Assays | en |
dc.subject | QSAR | en |
dc.subject | quantitative structure activity relation | en |
dc.subject | Models, Molecular | en |
dc.subject | biological activity | en |
dc.subject | drug identification | en |
dc.subject | Alzheimer's disease | en |
dc.subject | amyloid beta protein | en |
dc.subject | Amyloid beta-Peptides | en |
dc.subject | CP-ANN | en |
dc.subject | efenamic acid | en |
dc.subject | Fenamates | en |
dc.subject | In silico virtual screening | en |
dc.subject | Kohonen map | en |
dc.subject | pharmacophore | en |
dc.subject | Stereoisomerism | en |
dc.title | Ligand - Based virtual screening procedure for the prediction and the identification of novel β-amyloid aggregation inhibitors using Kohonen maps and Counterpropagation Artificial Neural Networks | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1016/j.ejmech.2010.11.029 | |
dc.description.volume | 46 | |
dc.description.issue | 2 | |
dc.description.startingpage | 497 | |
dc.description.endingpage | 508 | |
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 :63</p> | en |
dc.source.abbreviation | Eur.J.Med.Chem. | en |
dc.contributor.orcid | Koutentis, Panayiotis Andreas [0000-0002-4652-7567] | |
dc.contributor.orcid | Afantitis, Antreas [0000-0002-0977-8180] | |
dc.gnosis.orcid | 0000-0002-4652-7567 | |
dc.gnosis.orcid | 0000-0002-0977-8180 | |