Regional Explanations and Diverse Molecular Representations in Cheminformatics: A Comparative Study

In cheminformatics, the explainability of machine learning models is important for interpreting complex chemical data, deriving new chemical insights, and building trust in predictive models. However, cheminformatics datasets often exhibit clustered distributions, while traditional explanation methods might overlook intra-cluster variations and complicate the extraction of meaningful explanations.Additionally, diverse representations (tabular, sequence, image, and graph) yield divergent explanations. To address these issues, we propose a novel approach termed regional explanation, designed as an intermediate-level interpretability method that bridges the gap between local and global explanations. This approach systematically reveals how explanations and feature importance vary across data clusters. Using 2 public datasets, a graphene oxide nanoflakes dataset and QM9, with natural clustering properties, we comprehensively evaluate 4 molecular representations through tabular, sequence, image, and graph regional explanation, providing practical guidelines for representation selection. Our analysis illuminates complex, nonlinear relationships between molecular structures and predicted properties within clusters; explores the interplay among molecular features, feature importance, and target properties across distinct regions of chemical space; and advances the interpretability of machine learning models for complex molecular systems.