About: In silico approach to screen compounds active against parasitic nematodes of major socio-economic importance

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About: In silico approach to screen compounds active against parasitic nematodes of major socio-economic importance Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	In silico approach to screen compounds active against parasitic nematodes of major socio-economic importance
Creator	Khanna, Varun Ranganathan, Shoba
Source	Medline; PMC
abstract	BACKGROUND: Infections due to parasitic nematodes are common causes of morbidity and fatality around the world especially in developing nations. At present however, there are only three major classes of drugs for treating human nematode infections. Additionally the scientific knowledge on the mechanism of action and the reason for the resistance to these drugs is poorly understood. Commercial incentives to design drugs that are endemic to developing countries are limited therefore, virtual screening in academic settings can play a vital role is discovering novel drugs useful against neglected diseases. In this study we propose to build robust machine learning model to classify and screen compounds active against parasitic nematodes. RESULTS: A set of compounds active against parasitic nematodes were collated from various literature sources including PubChem while the inactive set was derived from DrugBank database. The support vector machine (SVM) algorithm was used for model development, and stratified ten-fold cross validation was used to evaluate the performance of each classifier. The best results were obtained using the radial basis function kernel. The SVM method achieved an accuracy of 81.79% on an independent test set. Using the model developed above, we were able to indentify novel compounds with potential anthelmintic activity. CONCLUSION: In this study, we successfully present the SVM approach for predicting compounds active against parasitic nematodes which suggests the effectiveness of computational approaches for antiparasitic drug discovery. Although, the accuracy obtained is lower than the previously reported in a similar study but we believe that our model is more robust because we intentionally employed stringent criteria to select inactive dataset thus making it difficult for the model to classify compounds. The method presents an alternative approach to the existing traditional methods and may be useful for predicting hitherto novel anthelmintic compounds.
has issue date	2011-11-30(xsd:dateTime)
bibo:doi	10.1186/1471-2105-12-s13-s25
bibo:pmid	22373185
has license	cc-by
sha1sum (hex)	35fa07c7c7d1803f22859f6647d5d743e7ad7652
schema:url	https://doi.org/10.1186/1471-2105-12-s13-s25
resource representing a document's title	In silico approach to screen compounds active against parasitic nematodes of major socio-economic importance
has PubMed Central identifier	PMC3278842
has PubMed identifier	22373185
schema:publication	BMC Bioinformatics
resource representing a document's body	covid:35fa07c7c7d1803f22859f6647d5d743e7ad7652#body_text
is schema:about of	named entity 'treating' named entity 'developing countries' named entity 'major' named entity 'mechanism' named entity 'nematodes' named entity 'parasitic' named entity 'MECHANISM OF ACTION' named entity 'ENDEMIC' named entity 'REASON FOR' named entity 'NEMATODES' named entity 'CAUSES' named entity 'APPROACH' named entity 'ROLE' named entity 'TREATING' named entity 'SETTINGS' named entity 'SCIENTIFIC' named entity 'ECONOMIC IMPORTANCE' named entity 'CLASSIFY' named entity 'COMMON' named entity 'MODEL' named entity 'NEMATODES' named entity 'MAJOR' named entity 'IN SILICO' named entity 'ACTIVE' named entity 'ACADEMIC' named entity 'HUMAN' named entity 'DEVELOPING COUNTRIES' named entity 'NEMATODE INFECTIONS' named entity 'SCREEN' named entity 'NEGLECTED DISEASES' named entity 'MACHINE LEARNING' named entity 'VITAL' named entity 'MAJOR' named entity 'PLAY' named entity 'RESISTANCE TO' named entity 'PARASITIC' named entity 'WORLD' named entity 'KNOWLEDGE' named entity 'INFECTIONS' named entity 'DEVELOPING NATIONS' named entity 'CLASSES' named entity 'DRUGS' named entity 'THESE' named entity 'USEFUL' named entity 'POORLY' named entity 'MORBIDITY' named entity 'VIRTUAL SCREENING' named entity 'DUE TO' named entity 'ROBUST' named entity 'COMMERCIAL' named entity 'PRESENT' named entity 'DESIGN DRUGS' named entity 'PARASITIC' named entity 'LIMITED' named entity 'SCREEN' named entity 'BUILD' named entity 'ACTIVE' named entity 'BACKGROUND' named entity 'STUDY' named entity 'NOVEL' named entity 'UNDERSTOOD' named entity 'INCENTIVES' covid:arg/35fa07c7c7d1803f22859f6647d5d743e7ad7652 named entity 'nematodes' named entity 'resistance' named entity 'Background' named entity 'parasitic nematodes' named entity 'benzimidazole' named entity 'parasitic nematodes' named entity 'active set' named entity 'benzimidazoles' named entity 'fenbendazole' named entity 'anthelmintics' named entity 'SVM' named entity 'experimental methods' named entity 'machine learning' named entity 'tubulin' named entity 'mefloquine' named entity 'reduce the dimensionality' named entity 'anthelmintic'

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