Evidence map›Paper›PMID 33200119›Full record

ArticleChemRxiv : the preprint server for chemistry2020

REDIAL-2020: A suite of machine learning models to estimate Anti-SARS-CoV-2 activities.

K C Govinda, Giovanni Bocci, Srijan Verma, Mahmudulla Hassan, Jayme Holmes, Jeremy J Yang, Suman Sirimulla, Tudor I Oprea

Open access · goldAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in ChemRxiv : the preprint server for chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed, 6 citations in OpenAlex.

No citing paper in PubMed yet.

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

8 authors at 1 institution in 1 country.

K C GovindaComputational Science Program, The University of Texas at El Paso, Texas 79968, USA.
Giovanni BocciTranslational Informatics Division, Department of Internal Medicine; University of New Mexico Health Sciences Center, Albuquerque, NM, USA.
Srijan VermaDepartment of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, Texas 79902, USA.
Mahmudulla HassanDepartment of Computer Science, The University of Texas at El Paso, Texas 79968, USA.
Jayme HolmesTranslational Informatics Division, Department of Internal Medicine; University of New Mexico Health Sciences Center, Albuquerque, NM, USA.
Jeremy J YangTranslational Informatics Division, Department of Internal Medicine; University of New Mexico Health Sciences Center, Albuquerque, NM, USA.
Suman SirimullaComputational Science Program, The University of Texas at El Paso, Texas 79968, USA.
Tudor I OpreaTranslational Informatics Division, Department of Internal Medicine; University of New Mexico Health Sciences Center, Albuquerque, NM, USA.
The University of Texas at El Paso · US

Funding

Machine Learning Model Validation for AD/ADRDU24CA224370 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI EDWARDS, JEREMY S · 2018 to 2023
$6.4M
NCI NIH HHS U24 CA224370
6 · The paper itself

Abstract

Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models for estimating small molecule activity from molecular structure, for a range of SARS-CoV-2 related assays. Each classifier is based on three distinct types of descriptors (fingerprint, physicochemical, and pharmacophore) for parallel model development. These models were trained using high throughput screening data from the NCATS COVID19 portal (https://opendata.ncats.nih.gov/covid19/index.html), with multiple categorical machine learning algorithms. The "best models" are combined in an ensemble consensus predictor that outperforms single models where external validation is available. This suite of machine learning models is available through the DrugCentral web portal (http://drugcentral.org/Redial). Acceptable input formats are: drug name, PubChem CID, or SMILES; the output is an estimate of anti-SARS-CoV-2 activities. The web application reports estimated activity across three areas (

Identifiers

PMID33200119
PMCPMC7668752
OpenAlexW3102359187

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.