AGENTIC-SYSTEMSJuly 16, 20261 min read

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

A new study proposes a Context-Augmented Prompting framework to help small language models (SLMs) overcome 'structural blindness' in molecular property prediction. By integrating graph-based tools, the method achieves up to a 74% accuracy improvement on the Tox21 dataset.

Computer Science > Artificial Intelligence

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

View PDF HTML (experimental)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.

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Source: arXiv cs.AI Recent

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