G
Georgia Melagraki
Researcher at Hellenic Military Academy
Publications - 99
Citations - 2730
Georgia Melagraki is an academic researcher from Hellenic Military Academy. The author has contributed to research in topics: Quantitative structure–activity relationship & Cheminformatics. The author has an hindex of 26, co-authored 89 publications receiving 2128 citations. Previous affiliations of Georgia Melagraki include University of Cyprus & National Technical University of Athens.
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Cheminformatics-aided discovery of small-molecule Protein-Protein Interaction (PPI) dual inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL).
Georgia Melagraki,Evangelos Ntougkos,Vagelis Rinotas,Christos Papaneophytou,Christos Papaneophytou,Georgios Leonis,Thomas Mavromoustakos,George Kontopidis,Eleni Douni,Antreas Afantitis,George Kollias +10 more
TL;DR: These compounds, namely T8 and T23, constitute the second and third published examples of dual small-molecule direct function inhibitors of TNF and RANKL, and could serve as lead compounds for the development of novel treatments for inflammatory and autoimmune diseases.
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Development and Evaluation of a QSPR Model for the Prediction of Diamagnetic Susceptibility
Antreas Afantitis,Georgia Melagraki,Haralambos Sarimveis,Panayiotis A. Koutentis,John Markopoulos,Olga Igglessi-Markopoulou +5 more
TL;DR: In this paper, a novel Multiple Linear Regression (MLR) model was developed and evaluated for the prediction of diamagnetic susceptibility using a database of 406 organic compounds involving a diverse set of chemical structures.
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Prediction of toxicity using a novel RBF neural network training methodology.
Georgia Melagraki,Antreas Afantitis,Kalliopi Makridima,Haralambos Sarimveis,Olga Igglessi-Markopoulou +4 more
TL;DR: A neural network methodology based on the radial basis function (RBF) architecture is introduced in order to establish quantitative structure-toxicity relationship models for the prediction of toxicity, which prove considerably more accurate than the MLR models.
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Investigation of substituent effect of 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides on CCR5 binding affinity using QSAR and virtual screening techniques.
Antreas Afantitis,Georgia Melagraki,Haralambos Sarimveis,Panayiotis A. Koutentis,John Markopoulos,Olga Igglessi-Markopoulou +5 more
TL;DR: A linear quantitative–structure activity relationship model is developed in this work using Multiple Linear Regression Analysis as applied to a series of 51 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides derivatives with CCR5 binding affinity, leading to a number of guanidine derivatives with significantly improved predicted activities.
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A nanoinformatics decision support tool for the virtual screening of gold nanoparticle cellular association using protein corona fingerprints
TL;DR: The development of a predictive model for the assessment of the biological response (cellular association) of surface-modified gold NPs, based on their physicochemical properties and protein corona fingerprints, utilizing a dataset of 105 unique NPs is reported.