About: Recommender Systems in Antiviral Drug Discovery

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Recommender Systems in Antiviral Drug Discovery
Creator	Fedorov, Maxim Nazarov, Ivan Nikitina, Anastasia Osolodkin, Dmitry Sosnin, Sergey Sosnina, Ekaterina
source	Medline; PMC
abstract	[Image: see text] Recommender systems (RSs), which underwent rapid development and had an enormous impact on e-commerce, have the potential to become useful tools for drug discovery. In this paper, we applied RS methods for the prediction of the antiviral activity class (active/inactive) for compounds extracted from ChEMBL. Two main RS approaches were applied: collaborative filtering (Surprise implementation) and content-based filtering (sparse-group inductive matrix completion (SGIMC) method). The effectiveness of RS approaches was investigated for prediction of antiviral activity classes (“interactions”) for compounds and viruses, for which some of their interactions with other viruses or compounds are known, and for prediction of interaction profiles for new compounds. Both approaches achieved relatively good prediction quality for binary classification of individual interactions and compound profiles, as quantified by cross-validation and external validation receiver operating characteristic (ROC) score >0.9. Thus, even simple recommender systems may serve as an effective tool in antiviral drug discovery.
has issue date	2020-06-21(xsd:dateTime)
bibo:doi	10.1021/acsomega.0c00857
bibo:pmid	32632398
has license	no-cc
sha1sum (hex)	4fdceb2c1a0d362f6eb3f1a700677b47f100f4d6
schema:url	https://doi.org/10.1021/acsomega.0c00857
resource representing a document's title	Recommender Systems in Antiviral Drug Discovery
has PubMed Central identifier	PMC7315437
has PubMed identifier	32632398
schema:publication	ACS Omega
resource representing a document's body	covid:4fdceb2c1a0d362f6eb3f1a700677b47f100f4d6#body_text
is schema:about of	named entity 'EFFECTIVE' named entity 'DRUG DISCOVERY' named entity 'EXTERNAL VALIDATION' named entity 'KNOWN' named entity 'APPROACHES' named entity 'ANTIVIRAL ACTIVITY' named entity 'VIRUSES' named entity 'INVESTIGATED' named entity 'TOOLS' named entity 'RAPID' named entity 'RSs' named entity 'matrix completion' named entity 'recommender systems' named entity 'viral species' named entity 'e-commerce' named entity 'viral species' named entity 'data sets' named entity 'ICTV' named entity 'ith' named entity 'genus' named entity 'cross-validation' named entity 'singular value decomposition' named entity 'viral species' named entity 'regularization' named entity 'matrix completion' named entity 'collaborative filtering' named entity 'logP' named entity 'ith' named entity 'data set' named entity 'Clustering algorithms' named entity 'data set' named entity 'ith' named entity 'viral species' named entity 'viral species' named entity 'Content-based filtering' named entity 'data set' named entity '0.9' named entity 'data set' named entity 'similarity matrix' named entity 'AUC values' named entity 'data set' named entity 'training set' named entity 'predictive models' named entity 'collaborative filtering' named entity 'cosine similarity' named entity 'CBF' named entity 'Frobenius norm' named entity 'Test set' named entity 'quality of models' named entity 'large data sets' named entity 'kNN' named entity 'algorithm' named entity 'data set' named entity 'data based' named entity 'receiver operating characteristic' named entity 'k-nearest neighbors' named entity 'algorithm' named entity 'viral species' named entity 'sklearn' named entity 'data set' named entity 'data set' named entity 'binary classification' named entity 'Cold-Start' named entity 'feature vectors' named entity 'ChEMBL' named entity 'information overload' named entity 'Scopus' named entity 'chemoinformatics' named entity 'cross-validation' named entity 'test set' named entity 'Collaborative filtering' named entity 'CBF' named entity 'sparse matrix' named entity 'e-commerce' named entity 'cold-start' named entity 'viral species'

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