Evidence map›Paper›PMID 36339714›Full record

ArticleFrontiers in molecular biosciences2022

Development of machine learning models for the screening of potential HSP90 inhibitors.

Mohd Imran Khan, Taehwan Park, Mohammad Azhar Imran, Venu Venkatarame Gowda Saralamma, Duk Chul Lee, Jaehyuk Choi, Mohammad Hassan Baig, Jae-June Dong

Open access · goldAbstract read
In one paragraph

Article in Frontiers in molecular biosciences, 2022. 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
0.3field-weighted citation impact, top 44% 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

0 citing papers in PubMed, 4 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 3 institutions in 1 country.

Mohd Imran KhanDepartment of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Taehwan ParkDepartment of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Mohammad Azhar ImranDepartment of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Venu Venkatarame Gowda SaralammaDepartment of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Duk Chul LeeDepartment of Family Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Jaehyuk ChoiBNJBiopharma, Yonsei University International Campus, Incheon, South Korea.
Mohammad Hassan BaigDepartment of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Jae-June DongDepartment of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Gangnam Severance Hospital · KRYonsei University · KRSeverance Hospital · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heat shock protein 90 (Hsp90) is a molecular chaperone playing a significant role in the folding of client proteins. This cellular protein is linked to the progression of several cancer types, including breast cancer, lung cancer, and gastrointestinal stromal tumors. Several oncogenic kinases are Hsp90 clients and their activity depends on this molecular chaperone. This makes HSP90 a prominent therapeutic target for cancer treatment. Studies have confirmed the inhibition of HSP90 as a striking therapeutic treatment for cancer management. In this study, we have utilized machine learning and different

Indexed as

cancerHsp90machine learingpharmacophorevirtual screeening

Identifiers

PMID36339714
PMCPMC9626531
OpenAlexW4306755494

What OpenQuestion holds

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LicenceCC BY
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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.