https://socictopen.socict.org/files/original/045a59dd2f01abdacd49768e1dccf644.pdf 7d2614c57e6ddc744f4cb0fe21f0c879 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Identification of potential antiviral compounds against SARS-CoV-2 structural and non structural protein targets: A pharmacoinformatics study of the CAS COVID-19 dataset. Creator An entity primarily responsible for making the resource Timothy Kirtek, Rolando García, Anas Hussain, Prasad Koduru, Murat Atis, Kathleen Wilson, Jason Y Park, Inimary Toby, Kimberly Diwa, Lavang Vu, Samuel Ho, Fajar Adnan, Ashley Nguyen, Andrew Cox, Patricia García, Yanhui Li, Heather Jones, Guanglu Shi, Allen Green, David Rosenbaum Description An account of the resource SARS-CoV-2 is a newly discovered virus which causes COVID-19 (coronavirus disease of 2019), initially documented as a human pathogen in 2019 in the city of Wuhan China, has now quickly spread across the globe with an urgency to develop effective treatments for the virus and emerging variants. Therefore, to identify potential therapeutics, an antiviral catalogue of compounds from the CAS registry, a division of the American Chemical Society was evaluated using a pharmacoinformatics approach. A total of 49,431 compounds were initially recovered. After a biological and chemical curation, only 23,575 remained. A machine learning approach was then used to identify potential compounds as inhibitors of SARS-CoV-2 based on a training dataset of molecular descriptors and fingerprints of known reported compounds to have favorable interactions with SARS-CoV-2. This approach identified 178 compounds, however, a molecular docking analysis revealed only 39 compounds with strong binding to active sites. Downstream molecular analysis of four of these compounds revealed various non-covalent interactions along with simultaneous modulation between ligand and protein active site pockets. The pharmacological profiles of these compounds showed potential drug-likeness properties. Our work provides a list of candidate anti-viral compounds that may be used as a guide for further investigation and therapeutic development against SARS-CoV-2. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource molecular docking, covid-19, SARS-CoV-2, molecular dynamics Identifier An unambiguous reference to the resource within a given context 10.1016/j.compbiomed.2021.104364 Source A related resource from which the described resource is derived Computers in biology and medicine