Molecular graph-based invariant representation learning with environmental inference and subgraph generation for out-of-distribution generalization.

Molecular representation learning (MRL) is a crucial link between machine learning and chemistry. It plays a vital role in predicting molecular properties and complex tasks such as drug discovery by encoding molecules as numerical vectors. While existing methods perform excellently when handling training and testing data from the same distribution, their generalization ability is often insufficient when faced with distribution shifts. Enhancing model generalization capability for out-of-distribution (OOD) data remains a significant challenge, as real-world molecular environments are often dynamic and uncertain. To effectively address this issue, we propose an innovative framework called EISG (Integrating Environmental Inference and Subgraph Generation) for molecular representation learning aimed at improving the performance of the model on OOD data by capturing the invariance of molecular graphs in different environments. Specifically, we introduce an unsupervised environmental classification model to identify latent variables generated by different distributions and designed a subgraph extractor based on information bottleneck theory to extracts invariant representations from molecular graphs that are closely related to the prediction labels. By combining new learning objectives, the environmental classifier and the subgraph extractor work in tandem to help the model identify invariant graph representations in different environments, leading to more robust OOD generalization. Experimental results demonstrate that our model exhibits strong generalization capabilities across various OOD settings. Code is available on GitHub.