Reconciling Selectivity Trends from a Comprehensive Kinase Inhibitor Profiling Campaign with Known Activity Data

Reconciling Selectivity Trends from a Comprehensive Kinase Inhibitor Profiling Campaign with Known Activity Data

Bibliographic Details
Main Authors:	Filip Miljković, Jürgen Bajorath
Format:	Article
Language:	English
Published:	American Chemical Society 2018-03-01
Series:	ACS Omega
Online Access:	http://dx.doi.org/10.1021/acsomega.8b00243

Similar Items

Exploring Selectivity of Multikinase Inhibitors across the Human Kinome
by: Filip Miljković, et al.
Published: (2018-01-01)

Data set of competitive and allosteric protein kinase inhibitors confirmed by X-ray crystallography
by: Huabin Hu, et al.
Published: (2021-04-01)

Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling
by: Atsushi Yoshimori, et al.
Published: (2022-01-01)

Data-Driven Exploration of Selectivity and Off-Target Activities of Designated Chemical Probes
by: Filip Miljković, et al.
Published: (2018-09-01)

Assessing the information content of structural and protein–ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning
by: Raquel Rodríguez-Pérez, et al.
Published: (2020-05-01)

Comprehensive Data-Driven Assessment of Non-Kinase Targets of Inhibitors of the Human Kinome
by: Mona Mobasher, et al.
Published: (2024-02-01)

Data sets of human and mouse protein kinase inhibitors with curated activity data including covalent inhibitors
by: Elena Xerxa, et al.
Published: (2023-10-01)

Kinase inhibitor data set for systematic analysis of representative kinases across the human kinome
by: Oliver Laufkötter, et al.
Published: (2020-10-01)

Systematic Analysis of Covalent and Allosteric Protein Kinase Inhibitors
by: Elena Xerxa, et al.
Published: (2023-08-01)

Multitask Machine Learning for Classifying Highly and Weakly Potent Kinase Inhibitors
by: Raquel Rodríguez-Pérez, et al.
Published: (2019-02-01)

Differentiating Inhibitors of Closely Related Protein Kinases with Single- or Multi-Target Activity via Explainable Machine Learning and Feature Analysis
by: Christian Feldmann, et al.
Published: (2022-04-01)

Comprehensive kinase profile of pacritinib, a nonmyelosuppressive Janus kinase 2 inhibitor
by: Singer J, et al.
Published: (2016-08-01)

CRISTA-NF measurements with unprecedented vertical resolution during the RECONCILE aircraft campaign
by: J. Ungermann, et al.
Published: (2012-05-01)

Extracting Compound Profiling Matrices from Screening Data
by: Martin Vogt, et al.
Published: (2018-04-01)

Kinase inhibitor selectivity profiling using differential scanning fluorimetry.
by: Fedorov, O, et al.
Published: (2012)

Data science and data analytics in life science research
by: Jürgen Bajorath
Published: (2023-12-01)

The known knowns and known unknowns of peacekeeping data
by: Dorussen, H, et al.
Published: (2016)

Identification and characterization of hundreds of potent and selective inhibitors of Trypanosoma brucei growth from a kinase-targeted library screening campaign.
by: Rosario Diaz, et al.
Published: (2014-10-01)

Clustering on Ranked Data for Campaign Selection
by: Umang Gupta, et al.
Published: (2020-01-01)

Predicting Isoform-Selective Carbonic Anhydrase Inhibitors via Machine Learning and Rationalizing Structural Features Important for Selectivity
by: Salvatore Galati, et al.
Published: (2021-01-01)

Prediction of Compound Profiling Matrices, Part II: Relative Performance of Multitask Deep Learning and Random Forest Classification on the Basis of Varying Amounts of Training Data
by: Raquel Rodríguez-Pérez, et al.
Published: (2018-09-01)

Data and code availability requirements in open science and consequences for different research environments
by: Jürgen Bajorath
Published: (2023-12-01)

Selectivity Profiling and Biological Activity of Novel β-Carbolines as Potent and Selective DYRK1 Kinase Inhibitors.
by: Katharina Rüben, et al.
Published: (2015-01-01)

Quantifying the tendency of therapeutic target proteins to bind promiscuous or selective compounds.
by: Ye Hu, et al.
Published: (2015-01-01)

WHEN WORLDS COLLIDE: RECONCILING MODELS, DATA, AND ANALYSIS
by: Rees, M, et al.
Published: (2009)

Reconciling Equational Heterogeneity within a Data Federation
by: Firat, Aykut, et al.
Published: (2011)

DIRECT : disovering [sic] and reconciling conflicts for data integration
Published: (2003)

Examining the Safety Profile of Janus Kinase (JAK) Inhibitors in the Management of Immune-Mediated Diseases: A Comprehensive Review
by: Krasimir Kraev, et al.
Published: (2023-11-01)

Bisimulation and refinement reconciled
by: Roscoe, A, et al.
Published: (2003)

Reconciling man with the enviroment /
by: 449600 Ashby, Eric
Published: (1978)

Will Development and Temperature be Reconciled?
by: Faradiba Faradiba, St. Fatimah Azzahra, Endah Yuniarti, Lodewik Zet, Tris Kurniawati Laia and Rini Wulandari
Published: (2024-03-01)

Understanding and reconciling the issues
by: Iris Levy, et al.
Published: (2022-10-01)

Reconciling Rights and Obligations
by: Pernille Ironside
Published: (2003-10-01)

KInhibition: A Kinase Inhibitor Selection Portal
by: Thomas Bello, et al.
Published: (2018-10-01)

Discovering and reconciling semantic conflicts : a data mining perspective
Published: (2003)

Kinase Inhibitor Profiling Reveals Unexpected Opportunities to Inhibit Disease-Associated Mutant Kinases
by: Krisna C. Duong-Ly, et al.
Published: (2016-02-01)

Global Research Trends in Tyrosine Kinase Inhibitors: Coword and Visualization Study
by: Jiming Hu, et al.
Published: (2022-04-01)

Compound data sets and software tools for chemoinformatics and medicinal chemistry applications: update and data transfer [v1; ref status: indexed, http://f1000r.es/32j]
by: Ye Hu, et al.
Published: (2014-03-01)

Versatile selective kinase inhibitors: Chemistry and biology of indirubins
by: Gaboriaud-Kolar, N, et al.
Published: (2012)

Characterization of isoenzyme-selective inhibitors of human sphingosine kinases.
by: Peng Gao, et al.
Published: (2012-01-01)