Machine learning-based classification models for non-covalent Bruton’s tyrosine kinase inhibitors: predictive ability and interpretability-Reference-Cited by-同舟云学术

Machine learning-based classification models for non-covalent Bruton’s tyrosine kinase inhibitors: predictive ability and interpretability

Published:2023-07-21 Issue: Volume: Page:
ISSN:1381-1991
Container-title:Molecular Diversity
language:en
Short-container-title:Mol Divers

Author:

Li Guo,Li Jiaxuan,Tian Yujia,Zhao Yunyang,Pang Xiaoyang,Yan Aixia

Publisher

Springer Science and Business Media LLC

Subject

Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Drug Discovery,Molecular Biology,General Medicine,Information Systems,Catalysis

Link

https://link.springer.com/content/pdf/10.1007/s11030-023-10696-6.pdf

Reference47 articles.

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2. Honigberg LA, Smith AM, Sirisawad M et al (2010) The Bruton tyrosine kinase inhibitor PCI-32765 blocks B-cell activation and is efficacious in models of autoimmune disease and B-cell malignancy. Proc Natl Acad Sci USA 107:13075–13080. https://doi.org/10.1073/pnas.1004594107

3. Burger JA, Wiestner A (2018) Targeting B cell receptor signalling in cancer: preclinical and clinical advances. Nat Rev Cancer 18:148–167. https://doi.org/10.1038/nrc.2017.121

4. Li X, Zuo Y, Tang G et al (2014) Discovery of a series of 2,5-diaminopyrimidine covalent irreversible inhibitors of Bruton’s tyrosine kinase with in vivo antitumor activity. J Med Chem 57:5112–5128. https://doi.org/10.1021/jm4017762

5. Wu J, Zhang M, Liu D (2016) Acalabrutinib (ACP-196): a selective second-generation BTK inhibitor. J Hematol Oncol 9:21. https://doi.org/10.1186/s13045-016-0250-9

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