Predictive Models for Fast and Effective Profiling of Kinase Inhibitors-Reference-Cited by-同舟云学术

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Predictive Models for Fast and Effective Profiling of Kinase Inhibitors

Published:2016-04-26 Issue:5 Volume:56 Page:895-905
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Bora Alina¹²,Avram Sorin²,Ciucanu Ionel¹,Raica Marius,Avram Stefana

Affiliation:

1. Department of Chemistry, Faculty of Chemistry-Biology-Geography, West University of Timisoara, 16 Pestalozzi Str., 300115, Timisoara, Romania

2. Department of Computational Chemistry, Institute of Chemistry, Timisoara of Romanian Academy, 24 Mihai Viteazu Avenue, Timisoara, 300223, Romania

Funder

Consiliul National al Cercetarii Stiintifice

European Social Fund

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.5b00646

Reference52 articles.

1. Targeting the cancer kinome through polypharmacology

2. The Protein Kinase Complement of the Human Genome

3. Kinase mutations in human disease: interpreting genotype–phenotype relationships

4. Classification and functional annotation of eukaryotic protein kinases

5. Navigating the kinome

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1. Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors;Journal of Cheminformatics;2024-04-16

2. Kinome-Wide Virtual Screening by Multi-Task Deep Learning;International Journal of Molecular Sciences;2024-02-22

3. Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors;Journal of Cheminformatics;2024-01-30

4. Artificial intelligence methods in kinase target profiling: Advances and challenges;Drug Discovery Today;2023-11

5. Meta-learning-based Inductive Logistic Matrix Completion for Prediction of Kinase Inhibitors;2023-06-22

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