Improving Molecular Property Prediction in Small Language Models Using Graph-based Tools

2026-07-14

AI Summary

This work addresses the limitation of small language models (SLMs) in perceiving critical graph topological structures when predicting molecular properties from SMILES strings. To overcome this, the authors propose a context-augmented prompting framework that dynamically integrates, during inference, prediction prompts generated by graph neural networks (GNNs) with interpretable subgraphs, thereby enabling structure-aware zero-shot molecular property prediction for the first time. The approach synergistically combines GNNs, subgraph extraction, confidence estimation, and edge-ablation intervention analysis. Evaluated on the MUTAG and Tox21 datasets, the method achieves up to a 74% relative improvement in accuracy, demonstrating conclusively that incorporating graph-based contextual information significantly enhances the molecular understanding capabilities of small language models.

Abstract

Small language models (SLMs) have shown promise for zero-shot molecular property prediction from SMILES strings, yet they often suffer from structural blindness because sequence representations under-specify key graph-topological cues. We propose a modular Context-Augmented Prompting framework that enables agentic tool use at inference time: a trained GNN expert model provides a predictive hint with confidence, and a GNN extracts an instance-specific explanatory subgraph (e.g., a subgraph SMILES and an accompanying explanatory paragraph). We evaluate three commonly used SLMs on MUTAG and Tox21 under five prompting configurations ranging from SMILES-only to using all available tools at hand. Across two datasets, enriching prompts with graph-derived context yields substantial accuracy gains, often exceeding 25% relative improvement and up to 74% on Tox21. We further validate the functional relevance of the extracted motifs via a necessity-based edge-drop intervention. Despite the observed gains, a persistent gap remains to specialized GNN models, highlighting both the value and limits of text-conditioned reasoning for molecular structure.

Problem

• molecular property prediction
• small language models
• structural blindness
• SMILES
• graph topology

Innovation

• Context-Augmented Prompting
• Graph Neural Networks
• Molecular Property Prediction
• Small Language Models
• Explainable Subgraph Extraction