This article is a Preprint
Preprints are preliminary research reports that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Preprints posted online allow authors to receive rapid feedback and the entire scientific community can appraise the work for themselves and respond appropriately. Those comments are posted alongside the preprints for anyone to read them and serve as a post publication assessment.
A machine learning platform to estimate anti-SARS-CoV-2 activities (preprint)
chemrxiv; 2021.
Preprint
in English
| PREPRINT-CHEMRXIV | ID: ppzbmed-10.26434.chemrxiv.12915779.v3
ABSTRACT
Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. Here we present "REDIAL-2020", a suite of computational models for estimating small molecule activities in a range of SARS-CoV-2 related assays. Models were trained using publicly available, high throughput screening data and by employing different descriptor types and various machine learning strategies. Here we describe the development and the usage of eleven models spanning across the areas of viral entry, viral replication, live virus infectivity, in vitro infectivity and human cell toxicity. REDIAL-2020 is available as a web application through the DrugCentral web portal (http//drugcentral.org/Redial). In addition, the web-app provides similarity search results that display the most similar molecules to the query, as well as associated experimental data. REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment.
Full text:
Available
Collection:
Preprints
Database:
PREPRINT-CHEMRXIV
Main subject:
Drug-Related Side Effects and Adverse Reactions
/
COVID-19
Language:
English
Year:
2021
Document Type:
Preprint
Similar
MEDLINE
...
LILACS
LIS