Volume 7, Issue 1 (6-2020)
jhbmi 2020, 7(1): 30-39 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Arian R, Mehri Dehnavi A, Ghasemi F. Improving biological activity prediction of protein kinase inhibitors using artificial neural network and partial least square methods. jhbmi 2020; 7 (1) :30-39
URL: http://jhbmi.ir/article-1-391-en.html
URL: http://jhbmi.ir/article-1-391-en.html
Ph.D. in Bioelectrics, Assistant Professor, Bioinformatics Dept., School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
Abstract: (2997 Views)
Introduction: Protein kinase causes many diseases, including cancer; therefore, inhibiting them plays an important role in the treatment of many diseases. Traditional discovery inhibitors of this enzyme is a time-consuming and costly process. Finding a reliable computer-aided drug discovery tools which can detect the inhibitors will reduce the cost. In this study, it is attempted to separate kinase inhibitors into two groups, active and inactive, using artificial neural network and finally predict biological activities of the predicted active compounds by partial least square .
Method: In this study, after extracting the molecular descriptors in order to avoid overfitting problem, dimensional reduction was applied using Genetic algorithm. Moreover, artificial neural network was applied to distinguish active compounds from inactive ones and the biological activities of the small molecules were predicted using partial least square linear regression.
Results: The results show that accuracy of the Neural networkmodel was improved from 74.45% to 86.7%, after reducing molecular descriptor dimensions. . The number of hidden nodes of this model was six with 86.7% accuracy, 83.4% sensitivity, 89.6% specificity and 73.2% Mathew's correlation coefficient. Moreover the partial least square linear regression model predicts the biological activity valuesby 85.8% correlation.
Conclusion: The Neural network model and the partial least square linear regression model can sufficiently predict Kinase inhibitors and Genetic algorithm will improve the models performance
Method: In this study, after extracting the molecular descriptors in order to avoid overfitting problem, dimensional reduction was applied using Genetic algorithm. Moreover, artificial neural network was applied to distinguish active compounds from inactive ones and the biological activities of the small molecules were predicted using partial least square linear regression.
Results: The results show that accuracy of the Neural networkmodel was improved from 74.45% to 86.7%, after reducing molecular descriptor dimensions. . The number of hidden nodes of this model was six with 86.7% accuracy, 83.4% sensitivity, 89.6% specificity and 73.2% Mathew's correlation coefficient. Moreover the partial least square linear regression model predicts the biological activity valuesby 85.8% correlation.
Conclusion: The Neural network model and the partial least square linear regression model can sufficiently predict Kinase inhibitors and Genetic algorithm will improve the models performance
Type of Study: Original Article |
Subject:
Bioinformatics
Received: 2019/04/22 | Accepted: 2019/11/2
Received: 2019/04/22 | Accepted: 2019/11/2
Audio File [MP3 1894 KB] (95 Download)
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
