About: Targeting SARS‐CoV‐2 RBD interface: a supervised computational data‐driven approach to identify potential modulators

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About: Targeting SARS‐CoV‐2 RBD interface: a supervised computational data‐driven approach to identify potential modulators Goto Sponge NotDistinct Permalink

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Targeting SARS‐CoV‐2 RBD interface: a supervised computational data‐driven approach to identify potential modulators
Creator	De Rosa, De Rosa, Maria De Simone, Giada De Simone, N Diana, Diana, Patrizia Gulotta, M Gulotta, Maria Gulotta, ] Lombino, J Lombino, Jessica Mekni, M Mekni, Nedra Padova, Padova, Alessandro Perricone, G Perricone, Ugo
source	Medline; PMC
abstract	Coronavirus Disease 2019 (COVID‐19) has spread out as a pandemic threat affecting over 2 million people. The infectious process initiates via binding of SARS‐CoV‐2 Spike (S) glycoprotein to host Angiotensin‐converting enzyme 2 (ACE2). The interaction is mediated by the receptor‐binding domain (RBD) of S glycoprotein, promoting host receptor recognition and binding to ACE2 peptidase domain (PD), thus representing a promising target for therapeutic intervention. Herein, we present a computational study aimed at identifying small molecules potentially able to target RBD. Although targeting PPI remains a challenge in drug discovery, our investigation highlights that interaction between SARS‐CoV‐2 RBD and ACE2 PD might be prone to small molecule modulation, due to the hydrophilic nature of the bi‐molecular recognition process and the presence of druggable hot spots. The fundamental objective is to identify, and provide to the international scientific community, hit molecules potentially suitable to enter the drug discovery process, preclinical validation and development.
has issue date	2020-07-23(xsd:dateTime)
bibo:doi	10.1002/cmdc.202000259
bibo:pmid	32700795
has license	no-cc
sha1sum (hex)	0b4dd1967b5d3157c6eb9c01ac59283bfcb93c75
schema:url	https://doi.org/10.1002/cmdc.202000259
resource representing a document's title	Targeting SARS‐CoV‐2 RBD interface: a supervised computational data‐driven approach to identify potential modulators
has PubMed Central identifier	PMC7405135
has PubMed identifier	32700795
schema:publication	ChemMedChem
resource representing a document's body	covid:0b4dd1967b5d3157c6eb9c01ac59283bfcb93c75#body_text
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