A GPU based virtual screening tool using SOM Online publication date: Thu, 08-Apr-2021
by P.B. Jayaraj; K.M. Mithun; G. Gopakumar; U.C.A. Jaleel
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 14, No. 1, 2021
Abstract: This paper attempts to introduce the applicability of low cost graphics processing unit alternatives to a virtual screening technique using a novel self-organising map (SOM) based technique. This method combines the unsupervised learning capability of the SOM with a subsequent supervised labelling of the trained SOM neurons for building the prediction model. This novel iteration-based SOM technique can label molecule as undefined classes which can reduce the false positives in the screening. For running large datasets, the serial implementation of the proposed algorithm is very time-consuming and cannot be completed in a stipulated time frame. This has been overcome by exploiting the parallelism present in finding the winner neuron and neuron weight updating steps. A tool named SOMSCREEN is developed based on the proposed parallelised method to make the drug discovery process faster. It is observed that, the proposed method offers reduced false positive rate than the Random Forest based work.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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 Computational Biology and Drug Design (IJCBDD):
Login with your Inderscience username and 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
Our Blog 
Follow us on Twitter 
Visit us on Facebook