{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:58:44Z","timestamp":1790657924333,"version":"4.1.0"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphar.2026.1910440","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:38:46Z","timestamp":1790656726000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Knowledge graph and machine learning-guided virtual screening prioritizes putative blockers of the PSMD11\/Rpn6-EBOV glycoprotein interface"],"prefix":"10.3389","volume":"17","author":[{"given":"Yanshuang","family":"Wang","sequence":"first","affiliation":[{"name":"National Key Laboratory of Advanced Biotechnology, Academy of Military Medical Sciences","place":["Beijing, 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