Computational discovery of marine natural phytochemicals as novel SIRT7 inhibitors for cancer treatment.

K M Tanjida Islam, Roksana Khanam, Shahin Mahmud

Abstract read

In one paragraph

Article in Journal, genetic engineering & biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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.

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Rapid screening of potent and mechanistically insightful repurposable anticancer drugs targeting EGFR for non-small cell lung cancer: machine learning-aided and structure-guided approach.Molecular diversity · 2026
Article

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

K M Tanjida IslamDepartment of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail-1902, Bangladesh.

Roksana KhanamDepartment of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail-1902, Bangladesh.

Shahin MahmudDepartment of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail-1902, Bangladesh. Electronic address: shahin018mbstu@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With cancer causing over 10 million deaths annually, identifying novel therapeutic targets is crucial. SIRT7 is a NAD-dependent deacetylase that regulates oncogenic pathways, making it a promising therapeutic target. However, no approved medications currently exist against SIRT7, and available inhibitors exhibit limited efficacy alongside significant toxicity. Therefore, this study explores marine biodiversity as a source of SIRT7 inhibitors. Molecular docking screening of SIRT7 identified four promising marine phytochemicals (CMNPD28383, CMNPD24305, CMNPD24304, CMNPD14924) with superior binding affinities (-9.9 to -8.4 kcal/mol). Molecular dynamics simulations confirmed stable protein-ligand complexes with RMSD variations of 4.714 to 6.905 Å. Most of these phytochemicals demonstrated favorable ADMET profiles, high oral bioavailability, strong predicted anticancer activity (Pa > 0,6), and potent machine learning (RandomForest Cross Validation R

Indexed as

CMNPD databaseInhibitorsMachine LearningMarineMolecular dockingMolecular dynamics simulationSIRT7

Identifiers

PMID41839676

DOI10.1016/j.jgeb.2025.100654

PMCPMC12810546

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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.