MolBioKG: Grounding Out-of-Graph Molecules in Biomedical Knowledge Graphs via Multi-Resolution Structural Anchoring

2026-08-06

AI Summary

This work addresses the cold-start problem in biomedical knowledge graphs, where out-of-graph molecules—provided only as SMILES strings—cannot be linked or reasoned over. To tackle this, the authors propose MolBioKG, a two-tier system that first constructs a static index of 2.74 million molecules based on scaffolds, fragments, functional groups, and molecular fingerprints, enabling multi-resolution structural anchoring and static multi-anchor retrieval via Reciprocal Rank Fusion. Building upon this, an adaptive, interpretable knowledge graph traversal is performed using a tool-augmented large language model (Adapt-KG). Requiring no task-specific training, the approach significantly improves multi-hop reasoning performance, raising Hits@10 from 0.585 to 0.876, and boosts target recall for out-of-graph molecules from 0.145 to 0.269, while ensuring predictions are grounded in structural anchors and traceable evidence.

Abstract

Biomedical knowledge graphs (KGs) accelerate drug discovery, but standard pipelines assume query molecules already exist as graph entities, leaving unregistered molecules disconnected. We address this cold-start challenge, termed the out-of-graph molecule problem, by introducing MolBioKG. This two-layer system grounds unseen molecules in biomedical evidence via multi-resolution structural anchoring. It connects an index of 2.74 million molecules (represented by scaffolds, fragments, functional groups, and fingerprints) to a 9.6-million-edge KG. Given only a SMILES string, MolBioKG retrieves structurally related graph entities and traverses their biomedical neighborhoods without task-specific training. It features two inference mechanisms: static multi-anchor retrieval using Reciprocal Rank Fusion, and Adapt-KG, a tool-using LLM policy for adaptive traversal. Evaluated across in-graph link recovery, complex multi-hop reasoning, and out-of-graph generalization, MolBioKG outperforms strong baselines. Notably, it raises Hits@10 from 0.585 to 0.876 in multi-hop reasoning and out-of-graph target recall from 0.145 to 0.269, all while ensuring predictions retain traceable structural anchors and source-attributed KG evidence.

Problem

• out-of-graph molecules
• biomedical knowledge graphs
• cold-start problem
• molecular grounding
• drug discovery

Innovation

• out-of-graph molecules
• multi-resolution structural anchoring
• biomedical knowledge graph
• adaptive KG traversal
• cold-start molecule grounding