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

In this study, we built classification models using machine learning techniques to predict the bioactivity of non-covalent inhibitors of Bruton’s tyrosine kinase (BTK) and to provide interpretable and transparent explanations for these predictions. To achieve this, we gathered data on BTK inhibitors...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Molecular diversity Jg. 28; H. 4; S. 2429 - 2447
Hauptverfasser:	Li, Guo, Li, Jiaxuan, Tian, Yujia, Zhao, Yunyang, Pang, Xiaoyang, Yan, Aixia
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.08.2024 Springer Nature B.V
Schlagworte:	Accuracy Agammaglobulinaemia Tyrosine Kinase - antagonists & inhibitors Agammaglobulinaemia Tyrosine Kinase - chemistry Algorithms Autoimmune diseases Binding sites Biochemistry Biological activity Biomedical and Life Sciences Chemical bonds Classification Clustering Datasets Decision trees Deep learning FDA approval Humans Life Sciences Lymphoma Machine Learning Neural networks Neural Networks, Computer Organic Chemistry Original Article Pharmacy Polymer Sciences Protein Kinase Inhibitors - chemistry Protein Kinase Inhibitors - pharmacology Signal transduction Support Vector Machine Support vector machines Tyrosine Kinase Inhibitors Non-covalent Bruton’s tyrosine kinase (BTK) inhibitors Machine learning method SHAP Classification models Structure clustering
ISSN:	1381-1991, 1573-501X, 1573-501X
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!