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8 results on '"Khiari J"'

2. Synthesis, biological evaluation and binding mode of new CLK1/DYRK1A inhibitors presenting a pyrido[3,4-g]quinazoline moiety

Author

TAZARKI, H., Zeinyeh, W., ESVAN, Y. J., Knapp, S., Chatterjee, D., Schröder, M., JOERGER, A. C., Khiari, J., JOSSELIN, B., Baratte, B., Bach, S., Ruchaud, S., ANIZON, F., Giraud, F., Moreau, P., Institut de Chimie de Clermont-Ferrand (ICCF), Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), and Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA)

Subjects
[CHIM]Chemical Sciences, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2021

3. Automated Setting of Bus Schedule Coverage Using Unsupervised Machine Learning

Author

Khiari, J, Moreira-Matias, L, Cerqueira, Vitor, Cats, O., Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., and Wang, R.

Subjects
Schedule, Computer science, Reliability (computer networking), Big data, Public transportation, Probabilistic reasoning, 010501 environmental sciences, computer.software_genre, Machine learning, Unsupervised learning, 01 natural sciences, 0502 economics and business, 11. Sustainability, Cluster analysis, 0105 earth and related environmental sciences, 050210 logistics & transportation, business.industry, 05 social sciences, Probabilistic logic, Automatic vehicle location, Schedule plan, Schedule coverage, Sequence mining, Metric (mathematics), Data mining, Artificial intelligence, business, computer
Abstract

The efficiency of Public Transportation (PT) Networks is a major goal of any urban area authority. Advances on both location and communication devices drastically increased the availability of the data generated by their operations. Adequate Machine Learning methods can thus be applied to identify patterns useful to improve the Schedule Plan. In this paper, the authors propose a fully automated learning framework to determine the best Schedule Coverage to be assigned to a given PT network based on Automatic Vehicle location (AVL) and Automatic Passenger Counting (APC) data. We formulate this problem as a clustering one, where the best number of clusters is selected through an ad-hoc metric. This metric takes into account multiple domain constraints, computed using Sequence Mining and Probabilistic Reasoning. A case study from a large operator in Sweden was selected to validate our methodology. Experimental results suggest necessary changes on the Schedule coverage. Moreover, an impact study was conducted through a large-scale simulation over the affected time period. Its results uncovered potential improvements of the schedule reliability on a large scale.

Published
2016

4. Automated Setting of Bus Schedule Coverage Using Unsupervised Machine Learning

Author

Khiari, J (author), Moreira-Matias, L (author), Cerqueira, Vitor (author), Cats, O. (author), Khiari, J (author), Moreira-Matias, L (author), Cerqueira, Vitor (author), and Cats, O. (author)

Abstract

The efficiency of Public Transportation (PT) Networks is a major goal of any urban area authority. Advances on both location and communication devices drastically increased the availability of the data generated by their operations. Adequate Machine Learning methods can thus be applied to identify patterns useful to improve the Schedule Plan. In this paper, the authors propose a fully automated learning framework to determine the best Schedule Coverage to be assigned to a given PT network based on Automatic Vehicle location (AVL) and Automatic Passenger Counting (APC) data. We formulate this problem as a clustering one, where the best number of clusters is selected through an ad-hoc metric. This metric takes into account multiple domain constraints, computed using Sequence Mining and Probabilistic Reasoning. A case study from a large operator in Sweden was selected to validate our methodology. Experimental results suggest necessary changes on the Schedule coverage. Moreover, an impact study was conducted through a large-scale simulation over the affected time period. Its results uncovered potential improvements of the schedule reliability on a large scale., Transport and Planning

Published
2016
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7. Strengthening Peptoid Helicity through Sequence Site-Specific Positioning of Amide cis -Inducing Nt Bu Monomers.

Author

Rzeigui M, Traikia M, Jouffret L, Kriznik A, Khiari J, Roy O, and Taillefumier C

Abstract

The synthesis of biomimetic helical secondary structures is sought after for the construction of innovative nanomaterials and applications in medicinal chemistry such as the development of protein-protein interaction modulators. Peptoids, a sequence-defined family of oligomers, enable a peptidomimetic strategy, especially considering the easily accessible monomer diversity and peptoid helical folding propensity. However, cis - trans isomerization of the backbone tertiary amides may impair the peptoid's adoption of stable secondary structures, notably the all- cis polyproline I-like helical conformation. Here, we show that cis -inducing Nt Bu achiral monomers strategically positioned within chiral sequences may reinforce the degree of peptoid helicity, although with a reduced content of chiral side chains. The design principles presented here will undoubtedly help achieve more conformationally stable helical peptoids with desired functions.

Published
2020
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8. New pyrido[3,4-g]quinazoline derivatives as CLK1 and DYRK1A inhibitors: synthesis, biological evaluation and binding mode analysis.

Author

Tazarki H, Zeinyeh W, Esvan YJ, Knapp S, Chatterjee D, Schröder M, Joerger AC, Khiari J, Josselin B, Baratte B, Bach S, Ruchaud S, Anizon F, Giraud F, and Moreau P

Subjects
Cell Line, Tumor, Chemistry Techniques, Synthetic, Humans, Molecular Docking Simulation, Protein Binding, Protein Conformation, Protein Kinase Inhibitors chemical synthesis, Protein Kinase Inhibitors chemistry, Protein Kinase Inhibitors metabolism, Protein Kinase Inhibitors pharmacology, Protein Serine-Threonine Kinases chemistry, Protein-Tyrosine Kinases chemistry, Quinazolines chemistry, Quinazolines metabolism, Structure-Activity Relationship, Dyrk Kinases, Drug Design, Protein Serine-Threonine Kinases antagonists & inhibitors, Protein Serine-Threonine Kinases metabolism, Protein-Tyrosine Kinases antagonists & inhibitors, Protein-Tyrosine Kinases metabolism, Quinazolines chemical synthesis, Quinazolines pharmacology
Abstract

Cdc2-like kinase 1 (CLK1) and dual specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) are involved in the regulation of alternative pre-mRNA splicing. Dysregulation of this process has been linked to cancer progression and neurodegenerative diseases, making CLK1 and DYRK1A important therapeutic targets. Here we describe the synthesis of new pyrido[3,4-g]quinazoline derivatives and the evaluation of the inhibitory potencies of these compounds toward CDK5, CK1, GSK3, CLK1 and DYRK1A. Introduction of aminoalkylamino groups at the 2-position resulted in several compounds with low nanomolar affinity and selective inhibition of CLK1 and/or DYRK1A. Their evaluation on several immortalized or cancerous cell lines showed varying degree of cell viability reduction. Co-crystal structures of CLK1 with two of the most potent compounds revealed two alternative binding modes of the pyrido[3,4-g]quinazoline scaffold that can be exploited for future inhibitor design., (Copyright © 2019 Elsevier Masson SAS. All rights reserved.)

Published
2019
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