M. Glick

发表

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

Which aspects of HTS are empirically correlated with downstream success?

Thomas J. Crisman, Z. Deng, A. Bender, 2008, Current opinion in drug discovery & development.

J. Jenkins, M. Glick, J. Davies, 2004, Journal of medicinal chemistry.

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

J. Jenkins, M. Glick, J. Davies, 2006 .

"Bayes Affinity Fingerprints" Improve Retrieval Rates in Virtual Screening and Define Orthogonal Bioactivity Space: When Are Multitarget Drugs a Feasible Concept?

Andreas Bender, Meir Glick, Jeremy L. Jenkins, 2006, J. Chem. Inf. Model..

Mapping adverse drug reactions in chemical space.

M. Milik, A. Bender, J. Jenkins, 2009, Journal of medicinal chemistry.

Bridging chemical and biological space: "target fishing" using 2D and 3D molecular descriptors.

Z. Deng, A. Bender, J. Jenkins, 2006, Journal of medicinal chemistry.

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.

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.

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

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

Identification of ligand binding sites on proteins using a multi-scale approach.

W. Richards, M. Glick, D. Robinson, 2002, Journal of the American Chemical Society.

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.

Prioritization of high throughput screening data of compound mixtures using molecular similarity

M. Glick, J. Davies, P. Acklin, 2003 .

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.

SPREAD—exploiting chemical features that cause differential activity behavior

Andreas Bender, Meir Glick, Ben Cornett, 2009 .

Diversity & tractability revisited in collaborative small molecule phenotypic screening library design.

M. Glick, A. Wassermann, M. Tudor, 2019, Bioorganic & medicinal chemistry.

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.

Interaction between the CD8 Coreceptor and Major Histocompatibility Complex Class I Stabilizes T Cell Receptor-Antigen Complexes at the Cell Surface*

Andrew K. Sewell, Meir Glick, Daniel C. Douek, 2005, Journal of Biological Chemistry.

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

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

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

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

Inside the Mind of a Medicinal Chemist: The Role of Human Bias in Compound Prioritization during Drug Discovery

Meir Glick, John D. Coley, Peter S. Kutchukian, 2012, PloS one.

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.

A lead discovery strategy driven by a comprehensive analysis of proteases in the peptide substrate space

Rajiv Chopra, Meir Glick, Florian Nigsch, 2010, Protein science : a publication of the Protein Society.

Understanding False Positives in Reporter Gene Assays: in Silico Chemogenomics Approaches To Prioritize Cell-Based HTS Data

Andreas Bender, Meir Glick, Jeremy L. Jenkins, 2007, J. Chem. Inf. Model..

Understanding False Positives in Reporter Gene Assays: In silico Chemogenomics Approaches to Prioritize Cell‐Based HTS Data.

M. Glick, 2007 .

Dual Allosteric Inhibition of SHP2 Phosphatase.

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

Public Domain HTS Fingerprints: Design and Evaluation of Compound Bioactivity Profiles from PubChem's Bioassay Repository

Anne Mai Wassermann, Meir Glick, Mateusz Maciejewski, 2016, J. Chem. Inf. Model..

Cover Picture: Analysis of Pharmacology Data and the Prediction of Adverse Drug Reactions and Off-Target Effects from Chemical Structure (ChemMedChem 6/2007)

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

Linking High-Throughput Screens to Identify MoAs and Novel Inhibitors of Mycobacterium tuberculosis Dihydrofolate Reductase.

Peter S. Kutchukian, M. Glick, M. Altman, 2017, ACS chemical biology.

Construction of Drug‐Like Compounds by Markov Chains

Eugen Lounkine, Meir Glick, Eugene I. Shakhnovich, 2013 .

Design, Synthesis, and Properties of a Potent Inhibitor of Pseudomonas aeruginosa Deacetylase LpxC.

M. Glick, L. Hamann, Yipin Lu, 2017, Journal of medicinal chemistry.

Small Molecules for Chemogenomicsbased Drug Discovery

J. Jenkins, M. Glick, J. Davies, 2006 .

Identification of TNO155, an Allosteric SHP2 Inhibitor for the Treatment of Cancer.

Sarah L. Williams, W. Sellers, Z. Deng, 2020, Journal of medicinal chemistry.

Novel drug targets in 2018

T. Oprea, M. Glick, O. Ursu, 2019, Nature Reviews Drug Discovery.

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..

Refined structure of bovine carboxypeptidase A at 1.25 A resolution.

M. Glick, A. Goldblum, G. Shoham, 2003, Acta crystallographica. Section D, Biological crystallography.

A mass spectrometry-based proteome map of drug action in lung cancer cell lines

Andy Liaw, M. Glick, Benjamin Ruprecht, 2020, Nature Chemical Biology.

The opportunities of mining historical and collective data in drug discovery.

Anne Mai Wassermann, Meir Glick, John W Davies, 2015, Drug discovery today.

CHEMGENIE: integration of chemogenomics data for applications in chemical biology.

Anne Mai Wassermann, Meir Glick, Dante A. Pertusi, 2018, Drug discovery today.

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.

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.

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.

Large Scale Meta-Analysis of Fragment-Based Screening Campaigns

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

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

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

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..

Prospective Assessment of Virtual Screening Heuristics Derived Using a Novel Fusion Score

Dante A. Pertusi, Peter S. Kutchukian, Gregory T O'Donnell, 2017, SLAS discovery : advancing life sciences R & D.

“Plate Cherry Picking”: A Novel Semi-Sequential Screening Paradigm for Cheaper, Faster, Information-Rich Compound Selection

Andreas Bender, Meir Glick, Christian N Parker, 2007, Journal of biomolecular screening.

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.

Prediction of Drug‐Likeness and its Integration into the Drug Discovery Process

Meir Glick, Ansgar Schuffenhauer, M. Glick, 2009 .

Enhanced immunogenicity of CTL antigens through mutation of the CD8 binding MHC class I invariant region

M. Glick, S. Hutchinson, A. Sewell, 2007, European journal of immunology.

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.

Docking of flexible molecules using multiscale ligand representations.

Meir Glick, W Graham Richards, Guy H Grant, 2002, Journal of medicinal chemistry.

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

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

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.

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

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

Novel CD8+ T Cell Antagonists Based on β2-Microglobulin*

Andrew K. Sewell, Meir Glick, Bent K. Jakobsen, 2002, The Journal of Biological Chemistry.

Flexible 3D pharmacophores as descriptors of dynamic biological space.

Andreas Bender, Meir Glick, Alex M Clark, 2007, Journal of molecular graphics & modelling.

Novel drug targets in 2018

Tudor I. Oprea, M. Glick, O. Ursu, 2019, Nature reviews. Drug discovery.

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..

CHEMOMETRIC APPLICATIONS OF NAÏVE BAYESIAN MODELS IN DRUG DISCOVERY

Eugen Lounkine, Meir Glick, Peter S. Kutchukian, 2013 .

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.

Efficient search of chemical space: navigating from fragments to structurally diverse chemotypes.

Anne Mai Wassermann, Eugen Lounkine, Meir Glick, 2013, Journal of medicinal chemistry.

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..

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.

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

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.

Efficient search of chemical space: navigating from fragments to structurally diverse chemotypes.

Peter S. Kutchukian, M. Glick, Eugen Lounkine, 2013, Journal of medicinal chemistry.

Chemotography for multi-target SAR analysis in the context of biological pathways.

Eugen Lounkine, Meir Glick, Paula Petrone, 2012, Bioorganic & medicinal chemistry.

"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.