George Kollias

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

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.

IKKβ in intestinal mesenchymal cells promotes initiation of colitis-associated cancer

Koliaraki et al. report that IKKβ deletion in ColVI-expressing intestinal mesenchymal cells protects mice against inflammation-induced intestinal carcinogenesis. In contrast, a companion study by Pallangyo et al. shows that deletion of IKKβ by the Col1a2CreER promoter in intestinal fibroblasts leads to increased colitis-induced tumorigenesis. The two studies suggest that targeting IKKβ in different fibroblast populations by using different promoters might have opposite outcomes in intestinal cancer.

A combined LS-SVM & MLR QSAR workflow for predicting the inhibition of CXCR3 receptor by quinazolinone analogs

A novel QSAR workflow is constructed that combines MLR with LS-SVM classification techniques for the identification of quinazolinone analogs as “active” or “non-active” CXCR3 antagonists. The accuracy of the LS-SVM classification technique for the training set and test was 100% and 90%, respectively. For the “active” analogs a validated MLR QSAR model estimates accurately their I-IP10 IC50 inhibition values. The accuracy of the QSAR model (R2 = 0.80) is illustrated using various evaluation techniques, such as leave-one-out procedure

Predictive QSAR workflow for the in silico identification and screening of novel HDAC inhibitors

{(R^{2}_{\rm LOO} =0.67)}

Novel indexes of heterogeneity of ventricular repolarization in subjects with early repolarization pattern.

and validation through an external test set

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)

{(R^{2}_{\rm pred} =0.78)}