Machine Learning Models Based on Molecular Fingerprints and an Extreme Gradient Boosting Method Lead to the Discovery of JAK2 Inhibitors-Reference-Cited by-同舟云学术

Machine Learning Models Based on Molecular Fingerprints and an Extreme Gradient Boosting Method Lead to the Discovery of JAK2 Inhibitors

Published:2019-11-20 Issue:12 Volume:59 Page:5002-5012
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Yang Minjian¹²,Tao Bingzhong²,Chen Chengjuan¹,Jia Wenqiang¹,Sun Shaolei²,Zhang Tiantai¹,Wang Xiaojian¹²^ORCID

Affiliation:

1. State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, P.R. China

2. Joint Laboratory of Artificial Intelligence of the Institute of Materia Medica and Yuan Qi Zhi Yao, Beijing 100050, P.R. China

Funder

Disciplines construction project

2018 Youth Talent Award Program for the Basic Scientific Research of the Medical College

CAMS Collaborative Innovation Project

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.9b00798

Reference53 articles.

1. JAKS AND STATS: Biological Implications

2. The JAK kinases: Not just another kinase drug discovery target

3. Preclinical characterization of the selective JAK1/2 inhibitor INCB018424: therapeutic implications for the treatment of myeloproliferative neoplasms

4. Ruxolitinib

5. Discovery of CP-690,550: A Potent and Selective Janus Kinase (JAK) Inhibitor for the Treatment of Autoimmune Diseases and Organ Transplant Rejection

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