AResKGLM: a graph-grounded language-model framework for interpretable multi-hop antimicrobial resistance reasoning.

Abstract

Antimicrobial resistance (AMR) threatens microbiology and microbiome bioinformatics because resistance phenotypes are shaped by interactions among genes, mobile genetic elements, and functional environments across microbial communities. Prioritizing resistance determinants requires models that reason across knowledge graphs (KGs) linking genes, proteins, pathways, drugs, and microbial phenotypes. Existing graph-based methods compress this evidence into scalar scores, whereas large language models can produce explanations not grounded in structured evidence. We developed AResKGLM (Antimicrobial Resistance Knowledge Graph Language Model), a graph-grounded language-model framework for interpretable microbial AMR bioinformatics that serializes breadth-first-search-retrieved multi-hop paths and per-entity biomedical descriptions into a structured Context-Path-Question prompt. Llama-3-8B and DeepSeek-R1-7B are adapted with QLoRA to produce binary link predictions and concise reasoning traces. On the KIDs benchmark, AResKGLM (Llama-3-8B) achieved F1 = 0.8482, outperforming KG-BERT (0.7213), NBFNet (0.5260), and ULTRA (0.2541) (paired Wilcoxon $p = 1.2 \times 10^{-7}$). Its advantage increased with reasoning depth: F1 decreased from 0.9197 at 2 hops to 0.8148 at 6 hops, whereas KG-BERT dropped from 0.8110 to 0.6716. Counterfactual path corruption produced an apparent F1 of 0.000, mechanically forced by the probe label assignment; the operative diagnostic is the per-sample flip rate (0.04-0.16), consistent with sensitivity to supplied biological evidence rather than reliance on pretrained priors alone. Cross-species evaluation yielded F1 = 0.81-0.88 with Matthews correlation coefficient (MCC) = 0.35-0.54 on Mycobacterium tuberculosis, Pseudomonas aeruginosa, and Staphylococcus aureus. Temporal ranking of 81 post-2022 gene-drug associations achieved Precision@20 = 100% and AUC-PR = 0.855. AResKGLM offers an interpretable, reproducible framework for multi-hop AMR reasoning, linking candidate prioritization with mechanism-oriented hypothesis generation.