Evidence map›Paper›PMID 42827386›Full record

ReviewChemical biology & drug design2026

A Multi-Scale Computational Framework for Anti-Persister Tuberculosis Drug Discovery.

Said Moshawih, Anguraj Moulishankar, Chee Yan Choo, Khang Wen Goh, Chrismawan Ardianto, Azam Che Idris, Long Chiau Ming

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Chemical biology & drug design, 2026. 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.

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

7 authors.

Said MoshawihFaculty of Pharmacy, Department of Pharmaceutical Sciences, Al-Ahliyya Amman University, Amman, Jordan.ORCID https://orcid.org/0000-0003-4840-0460
Anguraj MoulishankarDepartment of Pharmaceutical Chemistry, Integrated Drug Discovery Centre, Acharya & BM Reddy College of Pharmacy, Bengaluru, Karnataka, India.
Chee Yan ChooFaculty of Pharmacy, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia.ORCID https://orcid.org/0000-0002-9084-4818
Khang Wen GohFaculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia.ORCID https://orcid.org/0000-0001-6686-7019
Chrismawan ArdiantoFaculty of Pharmacy, Universitas Airlangga, Surabaya, Indonesia.ORCID https://orcid.org/0000-0003-3713-7900
Azam Che IdrisFaculty of Engineering and Technology, School of Computing and Artificial Intelligence, Sunway University, Sunway City, Malaysia.
Long Chiau MingFaculty of Pharmacy, Universitas Airlangga, Surabaya, Indonesia.ORCID https://orcid.org/0000-0002-6971-1383

Funding

Sunway University GRTIN-RAG(02)-DDSAI-02-2025
6 · The paper itself

Abstract

Latent tuberculosis (TB), caused by dormant, non-replicating Mycobacterium tuberculosis (Mtb), afflicts an estimated 1.7 billion individuals globally and remains a principal barrier to TB eradication. Dormant bacilli are phenotypically tolerant to conventional antibiotics and survive through continued dependence on oxidative phosphorylation (OXPHOS) and the electron transport chain (ETC), making bioenergetic enzymes, including the F1Fo-ATP synthase, cytochrome bcc:aa3 supercomplex, Type II NADH dehydrogenase (NDH-2) and cytochrome bd oxidase, compelling drug targets. This review proposes and rigorously analyses a synergistic, multi-scale computational workflow as a uniquely powerful paradigm for discovering anti-persister agents. The workflow cascades across four integrated stages: (1) dormancy-specific Quantitative Structure-Activity Relationship (QSAR) modelling using hypoxic assay data, with predictive models achieving R

Indexed as

Antitubercular AgentsDrug DiscoveryMycobacterium tuberculosisDrug DesignHumansMolecular Docking SimulationPharmacophoreQuantitative Structure-Activity RelationshipTuberculosisAntitubercular Agentsbioenergeticsdormant tuberculosismolecular dockingmolecular dynamics simulationmulti‐target drug discoveryquantitative structure–activity relationshipscaffold design

Identifiers

PMID42827386
PMCPMC13633470

What OpenQuestion 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 OpenQuestion graph.