Evidence map›Paper›PMID 42870962›Full record

ArticleFrontiers in pharmacology2026

Knowledge graph and machine learning-guided virtual screening prioritizes putative blockers of the PSMD11/Rpn6-EBOV glycoprotein interface.

Yanshuang Wang, Yuanjian Li, Lin Zhu, Zhiguo Jiang, Qian Tang, Hainan Liu, Yong Hu, Zijing Liu, Ziyi Li, Xinwei Zhou and 8 more

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 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
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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

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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

18 authors.

Yanshuang WangNational Key Laboratory of Advanced Biotechnology, Academy of Military Medical Sciences, Beijing, China.
Yuanjian LiSchool of Life Sciences and Medical Engineering, Anhui University, Hefei, China.
Lin ZhuNational Key Laboratory of Advanced Biotechnology, Academy of Military Medical Sciences, Beijing, China.
Zhiguo JiangSchool of Life Sciences and Medical Engineering, Anhui University, Hefei, China.
Qian TangSchool of Life Sciences and Medical Engineering, Anhui University, Hefei, China.
Hainan LiuNational Key Laboratory of Advanced Biotechnology, Academy of Military Medical Sciences, Beijing, China.
Yong HuNational Key Laboratory of Advanced Biotechnology, Academy of Military Medical Sciences, Beijing, China.
Zijing LiuNational Key Laboratory of Advanced Biotechnology, Academy of Military Medical Sciences, Beijing, China.
Ziyi LiSchool of Life Sciences and Medical Engineering, Anhui University, Hefei, China.
Xinwei ZhouNational Key Laboratory of Advanced Biotechnology, Academy of Military Medical Sciences, Beijing, China.
Xin QiNational Key Laboratory of Advanced Biotechnology, Academy of Military Medical Sciences, Beijing, China.
Yinan LiaoNational Key Laboratory of Advanced Biotechnology, Academy of Military Medical Sciences, Beijing, China.
Luya HuSchool of Life Sciences and Medical Engineering, Anhui University, Hefei, China.
Yufeng WangNational Key Laboratory of Advanced Biotechnology, Academy of Military Medical Sciences, Beijing, China.
Yue LiuNational Key Laboratory of Advanced Biotechnology, Academy of Military Medical Sciences, Beijing, China.
Ziqian AnNational Key Laboratory of Advanced Biotechnology, Academy of Military Medical Sciences, Beijing, China.
Qincai DongNational Key Laboratory of Advanced Biotechnology, Academy of Military Medical Sciences, Beijing, China.
Cheng CaoNational Key Laboratory of Advanced Biotechnology, Academy of Military Medical Sciences, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Host-directed antiviral discovery requires a traceable route from host-factor identification to testable molecular hypotheses. This study developed such a workflow around a host factor associated with Ebola virus glycoprotein (EBOV GP). Methods: GFP-GP pulldown coupled with co-immunoprecipitation/mass spectrometry was used to identify GP-associated host candidates. A PSMD11/Rpn6-centred workflow integrated knowledge-graph inference, ligand-only machine learning, ADMETlab profiling and interface-guided docking. Candidate prioritization was assessed through score-weight sensitivity analysis, applicability-domain assessment and docking controls addressing seed reproducibility, search convergence, whole-receptor context and macrocycle ring-conformer sensitivity. Docking scores were contextualized against a matched 59-compound library. Results: The analysis recovered 424 GP-pulldown-specific host proteins and nominated PSMD11/Rpn6 as a proteostasis anchor. The tier-gated knowledge-graph candidate membership was invariant across the tested weight perturbations, with Spearman rho = 0.998 under equal weighting. The selected ExtraTrees model achieved an AUC of 0.912 for the Ki endpoint. Applicability-domain assessment and ADMET profiling differentiated candidate confidence and predicted liabilities. Docking reproducibility checks yielded a maximum standard deviation of 0.05 kcal/mol for conformationally restricted ligands, while macrocyclic candidates showed greater protocol-dependent variability. Rule-based triage integrating upstream evidence, ADMET burden, chemotype diversity and mechanistic coverage returned five candidates: betamethasone, bafilomycin A1, everolimus, pitavastatin and CHEMBL1410015. Discussion: The workflow links a proteomic host-factor signal to a compact set of chemically diverse hypotheses with explicit evidence sources and limitations. The candidates remain hypotheses for experimental assessment; direct target engagement, interface blockade and antiviral activity are not established by the present study.

Indexed as

ADMETCo-IP/MSEBOV GPknowledge graphmachine learningmolecular dockingproteasomePSMD11

Identifiers

PMID42870962
PMCPMC13657989

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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.