Convolutional neural network with stacked autoencoders for predicting drug-target interaction and binding affinity
by Meriem Bahi; Mohamed Batouche
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 13, No. 1/2, 2021

Abstract: The prediction of novel drug-target interactions (DTIs) is critically important for drug repositioning, as it can lead the researchers to find new indications for existing drugs and to reduce the cost and time of the de novo drug development process. In order to explore new ways for this innovation, we have proposed two novel methods named SCA-DTIs and SCA-DTA, respectively to predict both drug-target interactions and drug-target binding affinities (DTAs) based on convolutional neural network (CNN) with stacked autoencoders (SAE). Initialising a CNN's weights with filters of trained stacked autoencoders yields to superior performance. Moreover, for boosting the performance of the DTIs prediction, we propose a new method called RNDTIs to generate reliable negative samples. Tests on different benchmark datasets show that the proposed method can achieve an excellent prediction performance with an accuracy of more than 99%. These results demonstrate the strength of the proposed model potential for DTIs and DTA prediction, thereby improving the drug repurposing process.

Online publication date: Tue, 09-Feb-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining, Modelling and Management (IJDMMM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com