Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Protein Kinase Inhibitors’ Classification Using K-Nearest Neighbor Algorithm Publisher Pubmed



Arian R1 ; Hariri A2 ; Mehridehnavi A1 ; Fassihi A2 ; Ghasemi F1
Authors
Show Affiliations
Authors Affiliations
  1. 1. Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. School of Pharmacology and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Computational Biology and Chemistry Published:2020


Abstract

Protein kinases are enzymes acting as a source of phosphate through ATP to regulate protein biological activities by phosphorylating groups of specific amino acids. For that reason, inhibiting protein kinases with an active small molecule plays a significant role in cancer treatment. To achieve this aim, computational drug design, especially QSAR model, is one of the best economical approaches to reduce time and save in costs. In this respect, active inhibitors are attempted to be distinguished from inactive ones using hybrid QSAR model. Therefore, genetic algorithm and K-Nearest Neighbor method were suggested as a dimensional reduction and classification model, respectively. Finally, to evaluate the proposed model's performance, support vector machine and Naive Bayesian algorithm were examined. The outputs of the proposed model demonstrated significant superiority to other QSAR models. © 2020 Elsevier Ltd
Related Docs
View other Related Docs
1. Study of Cxcr4 Chemokine Receptor Inhibitors Using Qspr and Molecular Docking Methodologies, Journal of Theoretical and Computational Chemistry (2019)
2. Deep Neural Network in Qsar Studies Using Deep Belief Network, Applied Soft Computing Journal (2018)
3. Identification of New Potential Candidates to Inhibit Egf Via Machine Learning Algorithm, European Journal of Pharmacology (2024)
4. Qsar Analysis of Some 1-(3,3-Diphenylpropyl)-Piperidinyl Amides and Ureas As Ccr5 Inhibitors Using Genetic Algorithm-Least Square Support Vector Machine, Medicinal Chemistry Research (2013)
5. Linear and Nonlinear Qsar Modeling of 1,3,8-Substituted-9-Deazaxanthines As Potential Selective A2bar Antagonists, Medicinal Chemistry Research (2013)
6. A Deep Learning Model Based on the Bert Pre-Trained Model to Predict the Antiproliferative Activity of Anti-Cancer Chemical Compounds, SAR and QSAR in Environmental Research (2024)
7. A Quantitative Structure-Activity Relationship (Qsar) Study of Some Diaryl Urea Derivatives of B-Raf Inhibitors, Research in Pharmaceutical Sciences (2016)
8. Identification of Essential 2D and 3D Chemical Features for Discovery of the Novel Tubulin Polymerization Inhibitors, Current Topics in Medicinal Chemistry (2019)
Experts (# of related papers)
View all Related Experts
Afshin Fassihi (13)
Lotfollah Saghaie Dehkordi (10)
Other Related Docs
9. Prediction of P38 Map Kinase Inhibitory Activity of 3, 4-Dihydropyrido [3, 2-D] Pyrimidone Derivatives Using an Expert System Based on Principal Component Analysis and Least Square Support Vector Machine, Research in Pharmaceutical Sciences (2014)
10. A Qsar Study of Some Cyclobutenediones As Ccr1 Antagonists by Artificial Neural Networks Based on Principal Component Analysis, DARU, Journal of Pharmaceutical Sciences (2011)
11. Classification and Similarity Analysis of Binding-Database: A Survey on Application of Multi-Class Classifiers for Deriving General Rules From Large Compound Databases, Journal of Isfahan Medical School (2017)
12. Qsar Study of Pett Derivatives As Potent Hiv-1 Reverse Transcriptase Inhibitors, Journal of Molecular Graphics and Modelling (2009)
13. In Silico Screening for Novel Tyrosine Kinase Inhibitors With Oxindole Scaffold As Anti-Cancer Agents: Design, Qsar Analysis, Molecular Docking and Admet Studies, Journal of Computational Biophysics and Chemistry (2022)
14. A Study on Quantitative Structure-Activity Relationship and Molecular Docking of Metalloproteinase Inhibitors Based on L-Tyrosine Scaffold, DARU, Journal of Pharmaceutical Sciences (2015)
15. Quantitative Structure Activities Relationships of Some 2-Mercaptoimidazoles As Ccr2 Inhibitors Using Genetic Algorithm-Artificial Neural Networks, Research in Pharmaceutical Sciences (2013)
16. Qsar Study of Anthranilic Acid Sulfonamides As Methionine Aminopeptidase-2 Inhibitors, Monatshefte fur Chemie (2012)
17. Application of an Expert System Based on Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System (Ga-Anfis) in Qsar of Cathepsin K Inhibitors, Expert Systems with Applications (2012)
18. Qsar and Docking Analysis of A2b Adenosine Receptor Antagonists Based on Non-Xanthine Scaffold, Medicinal Chemistry Research (2015)
19. Qsar Study of Some Ccr5 Antagonists As Anti-Hiv Agents Using Radial Basis Function Neural Network and General Regression Neural Network on the Basis of Principal Components, Medicinal Chemistry Research (2012)
20. Qsar Study of P56 Lck Protein Tyrosine Kinase Inhibitory Activity of Flavonoid Derivatives Using Mlr and Ga-Pls, International Journal of Molecular Sciences (2008)
21. Application of Partial Least Squares and Radial Basis Function Neural Networks in Multivariate Imaging Analysis-Quantitative Structure Activity Relationship: Study of Cyclin Dependent Kinase 4 Inhibitors, Journal of Molecular Graphics and Modelling (2010)