EviAlign: Signed Re-Interpretation of Biomedical Literature Graphs for Mechanism-Aware Drug Repurposing

Background/Objectives: Literature-derived knowledge graphs are attractive for drug repurposing because every edge retains its supporting publications. Their predicates, however, record which relation a sentence asserted, not whether the evidence supports a therapeutic direction. We asked whether reading those publications can align such a graph with the repurposing task. Methods: EviAlign re-interprets each edge of disease-centered SemMedDB subgraphs. A large language model (LLM) judge reads the supporting publications sentence by sentence, assigns a therapeutic direction or withdraws the edge, and weights each edge by evidence strength rather than publication count. Signed edges compose along drug–mediator–disease paths. Six diseases and 68,689 edges were evaluated against the original predicates read through a fixed mapping and trimmed to the same size, using curated drug–disease benchmarks, an internal sign-consistency test, and blinded rating by three domain experts. Results: EviAlign increased average precision by 63% over the size-matched fixed-mapping baseline. For candidates with no direct disease literature, Precision@10 increased approximately threefold (0.050 versus 0.018). Direct and mediator-derived signs agreed in 75.0% of evaluable cases, 16 percentage points above a shuffled-label control; the fixed mapping stayed at the level of its own control. Experts confirmed the re-interpreted direction in 34 of 36 reversal ratings and judged 80% of top-ranked mediator-only candidates worth investigating, versus 53% of low-ranked controls. Conclusions: Reading the evidence behind each edge aligns literature-derived knowledge graphs with drug repurposing and yields interpretable, mechanism-grounded hypotheses for further investigation.