In silico prediction of anti-malarial hit molecules based on machine learning methods Online publication date: Sun, 12-Apr-2015
by Madhulata Kumari; Subhash Chandra
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 8, No. 1, 2015
Abstract: Machine learning techniques have been widely used in drug discovery and development in the areas of cheminformatics. Aspartyl aminopeptidase (M18AAP) of Plasmodium falciparum is crucial for survival of malaria parasite. We have created predictive models using weka and evaluated their performance based on various statistical parameters. Random Forest based model was found to be the most specificity (97.94%), with best accuracy (97.3%), MCC (0.306) as well as ROC (86.1%). The accuracy and MCC of these models indicated that they could be used to classify huge dataset of unknown compounds to predict their antimalarial compounds to develop effective drugs. Further, we deployed best predictive model on NCI diversity set IV. As result we found 59 bioactive anti-malarial molecules inhibiting M18AAP. Further, we obtained 18 non-toxic hit molecules out of 59 bioactive compounds. We suggest that such machine learning approaches could be applied to reduce the cost and length of time of drug discovery.
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