Identifying Semantic Component for Robust Molecular Property Prediction

TL;DR

This paper introduces SCI, a generative model that explicitly identifies semantic components in molecular data, enhancing out-of-distribution generalization and achieving state-of-the-art results across multiple benchmarks.

Contribution

The paper proposes a novel generative model with semantic-component identifiability for molecular property prediction, improving OOD generalization and interpretability.

Findings

Achieves state-of-the-art performance on 21 datasets.

Demonstrates improved OOD generalization over existing methods.

Provides insightful visualizations and explanations for predictions.

Abstract

Although graph neural networks have achieved great success in the task of molecular property prediction in recent years, their generalization ability under out-of-distribution (OOD) settings is still under-explored. Different from existing methods that learn discriminative representations for prediction, we propose a generative model with semantic-components identifiability, named SCI. We demonstrate that the latent variables in this generative model can be explicitly identified into semantic-relevant (SR) and semantic-irrelevant (SI) components, which contributes to better OOD generalization by involving minimal change properties of causal mechanisms. Specifically, we first formulate the data generation process from the atom level to the molecular level, where the latent space is split into SI substructures, SR substructures, and SR atom variables. Sequentially, to reduce misidentification, we restrict the minimal changes of the SR atom variables and add a semantic latent substructure regularization to mitigate the variance of the SR substructure under augmented domain changes. Under mild assumptions, we prove the block-wise identifiability of the SR substructure and the comment-wise identifiability of SR atom variables. Experimental studies achieve state-of-the-art performance and show general improvement on 21 datasets in 3 mainstream benchmarks. Moreover, the visualization results of the proposed SCI method provide insightful case studies and explanations for the prediction results. The code is available at: https://github.com/DMIRLAB-Group/SCI.