Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction

TL;DR

This paper introduces a data-efficient molecular property prediction method using a hierarchical grammar that models molecular geometry, enabling better predictions especially with limited labeled data.

Contribution

It proposes a learnable hierarchical molecular grammar that induces a geometry of molecular graphs, improving property prediction with limited data.

Findings

Outperforms baseline models on various datasets

Effective in scenarios with extremely limited data

Demonstrates the benefit of grammar-induced geometry in molecular prediction

Abstract

The prediction of molecular properties is a crucial task in the field of material and drug discovery. The potential benefits of using deep learning techniques are reflected in the wealth of recent literature. Still, these techniques are faced with a common challenge in practice: Labeled data are limited by the cost of manual extraction from literature and laborious experimentation. In this work, we propose a data-efficient property predictor by utilizing a learnable hierarchical molecular grammar that can generate molecules from grammar production rules. Such a grammar induces an explicit geometry of the space of molecular graphs, which provides an informative prior on molecular structural similarity. The property prediction is performed using graph neural diffusion over the grammar-induced geometry. On both small and large datasets, our evaluation shows that this approach outperforms a wide spectrum of baselines, including supervised and pre-trained graph neural networks. We include a detailed ablation study and further analysis of our solution, showing its effectiveness in cases with extremely limited data. Code is available at https://github.com/gmh14/Geo-DEG.