Data Sheet 4_Knowledge graph and machine learning-guided virtual screening prioritizes putative blockers of the PSMD11/Rpn6-EBOV glycoprotein interface.csv

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.</p>Discussion<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.</p>