About: Predicting novel drugs for SARS-CoV-2 using machine learning from a >10 million chemical space

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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
title	Predicting novel drugs for SARS-CoV-2 using machine learning from a >10 million chemical space
Creator	Ray, Anandasankar Kowalewski, Q6, Joel
source	Elsevier; Medline; PMC
abstract	There is an urgent need for the identification of effective therapeutics for COVID-19 and we have developed a machine learning drug discovery pipeline to identify several drug candidates. First, we collect assay data for 65 target human proteins known to interact with the SARS-CoV-2 proteins, including the ACE2 receptor. Next, we train machine learning models to predict inhibitory activity and use them to screen FDA registered chemicals and approved drugs (∼100,000) and ∼14 million purchasable chemicals. We filter predictions according to estimated mammalian toxicity and vapor pressure. Prospective volatile candidates are proposed as novel inhaled therapeutics since the nasal cavity and respiratory tracts are early bottlenecks for infection. We also identify candidates that act across multiple targets as promising for future analyses. We anticipate that this theoretical study can accelerate testing of two categories of therapeutics: repurposed drugs suited for short-term approval, and novel efficacious drugs suitable for a long-term follow up.
has issue date	2020-08-06(xsd:dateTime)
bibo:doi	10.1016/j.heliyon.2020.e04639
bibo:pmid	32802980
has license	no-cc
sha1sum (hex)	b966d377a5ac89cf1d9824941a1eac0636a93294
schema:url	https://doi.org/10.1016/j.heliyon.2020.e04639
resource representing a document's title	Predicting novel drugs for SARS-CoV-2 using machine learning from a >10 million chemical space
has PubMed Central identifier	PMC7409807
has PubMed identifier	32802980
schema:publication	Heliyon
resource representing a document's body	covid:b966d377a5ac89cf1d9824941a1eac0636a93294#body_text
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