  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.
