Evidence map›Paper›PMID 34339864›Full record

ReviewDrug discovery today2021

Advances in the computational landscape for repurposed drugs against COVID-19.

Illya Aronskyy, Yosef Masoudi-Sobhanzadeh, Antonio Cappuccio, Elena Zaslavsky

Open access · greenAbstract readReview
In one paragraph

Review in Drug discovery today, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
4.7field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 33 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Illya AronskyyDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Yosef Masoudi-SobhanzadehResearch Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran.
Antonio CappuccioDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. Electronic address: antonio.cappuccio@mssm.edu.
Elena ZaslavskyDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. Electronic address: elena.zaslavsky@mssm.edu.
Icahn School of Medicine at Mount Sinai · USTabriz University of Medical Sciences · IR

Funding

Project I: Immunity to Influenza in Primary Lung Epithelial CellsU19AI117873 · NIAID · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI SEALFON, STUART C. · 2015 to 2019
$8.6M
NIAID NIH HHS U19 AI117873
6 · The paper itself

Abstract

The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.

Indexed as

Computational BiologyCOVID-19 Drug TreatmentAntiviral AgentsComputer SimulationDatabases, FactualDrug RepositioningHumansMachine LearningMolecular Docking SimulationAntiviral AgentsComputational drug repurposingCOVID-19Docking and molecular dynamicsSARS-CoV-2Structure-guided machine learningVirus–host interaction network analysis

Identifiers

PMID34339864
PMCPMC8323501
OpenAlexW3190725941

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.