Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms

Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms

Cysteinyl leukotrienes 1 (CysLT1) receptor is a promising drug target for rhinitis or other allergic diseases. In our study, we built classification models to predict bioactivities of CysLT1 receptor antagonists. We built a dataset with 503 CysLT1 receptor antagonists which were divided into two gro...

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Veröffentlicht in:Molecular diversity 2021-08, Vol.25 (3), p.1597-1616
Hauptverfasser: Wang, Hongzhao, Qin, Zijian, Yan, Aixia
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Binding Sites
Biochemistry
Biomedical and Life Sciences
Cheminformatics - methods
Drug Discovery
Leukotriene Antagonists - chemistry
Leukotriene Antagonists - pharmacology
Life Sciences
Machine Learning
Models, Molecular
Molecular Structure
Neural networks
Organic Chemistry
Original Article
Pharmacy
Polymer Sciences
Protein Binding
Quantitative Structure-Activity Relationship
Receptors, Leukotriene - chemistry
Reproducibility of Results
ROC Curve
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Zusammenfassung:Cysteinyl leukotrienes 1 (CysLT1) receptor is a promising drug target for rhinitis or other allergic diseases. In our study, we built classification models to predict bioactivities of CysLT1 receptor antagonists. We built a dataset with 503 CysLT1 receptor antagonists which were divided into two groups: highly active molecules (IC 50  
ISSN:1381-1991
1573-501X
DOI:10.1007/s11030-020-10165-4