Molecular descriptors are central to the performance and interpretability of QSPR models, yet most existing fingerprints for organic electronics lack chemical relevance or interpretability. Here, we present the Organic Electronic Fingerprint (OEFP), a structure-based representation tailored for OLED and OPV materials. OEFP was constructed from a manually curated OLED dataset and publicly available OPV and chromophore datasets to ensure structural diversity. Synthetically accessible substructures were identified using fragmentation and ring decomposition methods, which capture the conjugated π-bonds crucial for organic electronic materials, and subsequently encoded as individual bits. In case studies of OPV HOMO energy prediction, OEFP achieved up to 13.7% lower MAE and 1.6% higher R2 compared to domain-mismatched structure-based fingerprint under random splits. Under scaffold-based splits designed to assess generalization, OEFP demonstrated substantially improved generalization, reducing MAE by 50-70% and increasing R2 by over 150% relative to the same baseline and achieved performance comparable to ECFP baselines evaluated at a similar fingerprint length when using count-based representations. SHAP analysis further enabled intuitive substructure-level interpretation, while OEFP's chemically meaningful and synthetically relevant design supports rational molecular generation. Together, these results establish OEFP as an effective and interpretable molecular representation for machine learning applications in organic electronics.
Yang et al. (Fri,) studied this question.