Meir Glick

发表

Crystallization and preliminary X-ray structural studies of a high-affinity CD8alphaalpha co-receptor to pMHC.

G. Gao, M. Glick, J. Bell, 2005, Acta crystallographica. Section F, Structural biology and crystallization communications.

The CD8 T Cell Coreceptor Exhibits Disproportionate Biological Activity at Extremely Low Binding Affinities*

Andrew K. Sewell, Meir Glick, David A. Price, 2003, Journal of Biological Chemistry.

Computational design and crystal structure of an enhanced affinity mutant human CD8 αα coreceptor

G. Gao, M. Glick, J. Bell, 2007 .

Gaining Insight into Off-Target Mediated Effects of Drug Candidates with a Comprehensive Systems Chemical Biology Analysis

Bin Chen, Andreas Bender, Meir Glick, 2009, J. Chem. Inf. Model..

The role of computational methods in the identification of bioactive compounds.

Meir Glick, Edgar Jacoby, M. Glick, 2011, Current opinion in chemical biology.

High-Throughput Screen for Inhibitors of Klebsiella pneumoniae Virulence Using a Tetrahymena pyriformis Co-Culture Surrogate Host Model

M. Glick, J. Jansen, C. Dean, 2022, ACS omega.

A novel energy‐based stochastic method for positioning polar protons in protein structures from X‐rays

A. Goldblum, Meir Glick, 2000, Proteins.

A stochastic algorithm for global optimization and for best populations: A test case of side chains in proteins

Amiram Goldblum, Meir Glick, M. Glick, 2002, Proceedings of the National Academy of Sciences of the United States of America.

Dual Allosteric Inhibition of SHP2 Phosphatase.

Sarah L. Williams, S. Blacklow, M. Glick, 2018, ACS chemical biology.

From in silico target prediction to multi-target drug design: current databases, methods and applications.

Andreas Bender, Meir Glick, Alexios Koutsoukas, 2011, Journal of proteomics.

"Virtual fragment linking": an approach to identify potent binders from low affinity fragment hits.

Andreas Bender, Meir Glick, Mariusz Milik, 2008, Journal of medicinal chemistry.

A screening pattern recognition method finds new and divergent targets for drugs and natural products.

Anne Mai Wassermann, Eugen Lounkine, Meir Glick, 2014, ACS chemical biology.

Large Scale Meta-Analysis of Fragment-Based Screening Campaigns

Anne Mai Wassermann, Meir Glick, S Kirk Wright, 2015, Journal of biomolecular screening.

Application of Machine Learning To Improve the Results of High-Throughput Docking Against the HIV-1 Protease

Meir Glick, John W. Davies, Anthony E. Klon, 2004, J. Chem. Inf. Model..

Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results.

Anthony E. Klon, M. Glick, J. Davies, 2004, Journal of medicinal chemistry.

Finding more needles in the haystack: A simple and efficient method for improving high-throughput docking results.

Anthony E. Klon, M. Glick, J. Davies, 2004, Journal of medicinal chemistry.

Enrichment of High-Throughput Screening Data with Increasing Levels of Noise Using Support Vector Machines, Recursive Partitioning, and Laplacian-Modified Naive Bayesian Classifiers

Meir Glick, Jeremy L. Jenkins, John W. Davies, 2006, J. Chem. Inf. Model..

Use of ligand based models for protein domains to predict novel molecular targets and applications to triage affinity chromatography data.

John A. Tallarico, A. Bender, J. Jenkins, 2009, Journal of proteome research.

SPREAD—exploiting chemical features that cause differential activity behavior

Andreas Bender, Meir Glick, Ben Cornett, 2009 .

Experimental error, kurtosis, activity cliffs, and methodology: What limits the predictivity of QSAR models?

Meir Glick, Robert P Sheridan, Yuting Xu, 2020, Journal of chemical information and modeling.

Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases

Meir Glick, Jeremy L. Jenkins, John W. Davies, 2006, J. Chem. Inf. Model..

Application of Machine Learning To Improve the Results of High-Throughput Docking Against the HIV-1 Protease

Anthony E. Klon, M. Glick, J. Davies, 2004, Journal of Chemical Information and Modeling.

Streamlining lead discovery by aligning in silico and high-throughput screening.

Meir Glick, Jeremy L Jenkins, John W Davies, 2006, Current opinion in chemical biology.

Enrichment of Extremely Noisy High-Throughput Screening Data Using a Naïve Bayes Classifier

Meir Glick, Pierre Acklin, Anthony E. Klon, 2004, Journal of biomolecular screening.

PepSeA: Peptide Sequence Alignment and Visualization Tools to Enable Lead Optimization

Danny A. Bitton, A. Wassermann, O. Ursu, 2021, bioRxiv.

Potent, Selective, and Orally Bioavailable Inhibitors of VPS34 Provide Chemical Tools to Modulate Autophagy in Vivo.

P. Finan, M. Glick, P. Furet, 2016, ACS medicinal chemistry letters.

Biodiversity of small molecules--a new perspective in screening set selection.

Anne Mai Wassermann, Eugen Lounkine, Meir Glick, 2013, Drug discovery today.

Andreas Bender, Meir Glick, Jeremy L. Jenkins, 2009, J. Chem. Inf. Model..

Computational methods for early predictive safety assessment from biological and chemical data

Meir Glick, Florian Nigsch, Ben Cornett, 2011, Expert opinion on drug metabolism & toxicology.

Analysis of Pharmacology Data and the Prediction of Adverse Drug Reactions and Off‐Target Effects from Chemical Structure

A. Bender, J. Jenkins, J. Scheiber, 2007, ChemMedChem.

Dark chemical matter as a promising starting point for drug lead discovery.

Anne Mai Wassermann, Eugen Lounkine, Meir Glick, 2015, Nature chemical biology.

Plate-Based Diversity Selection Based on Empirical HTS Data to Enhance the Number of Hits and Their Chemical Diversity

Andreas Bender, Meir Glick, Dmitri Mikhailov, 2009, Journal of biomolecular screening.

Anne Mai Wassermann, Eugen Lounkine, Meir Glick, 2013, J. Chem. Inf. Model..

Rethinking molecular similarity: comparing compounds on the basis of biological activity.

Peter S. Kutchukian, Z. Deng, J. Jenkins, 2012, ACS chemical biology.

Stable, soluble T-cell receptor molecules for crystallization and therapeutics.

Meir Glick, Bent K Jakobsen, M. Glick, 2003, Protein engineering.