A Multi-View Fusion Data-Augmented Method for Predicting BODIPY Dye Spectra

Fluorescent molecules, particularly BODIPY dyes, have found wide applications in fields such as bioimaging and optoelectronics due to their excellent photostability and tunable spectral properties. In recent years, artificial intelligence methods have enabled more efficient screening of molecules, allowing the required molecules to be quickly obtained. However, existing methods remain inadequate to meet research needs, primarily due to incomplete molecular feature extraction and the scarcity of data under small-sample conditions. In response to the aforementioned challenges, this paper introduces a spectral prediction method that integrates multi-view feature fusion and data augmentation strategies. The proposed method consists of three modules. The molecular feature engineering module constructs a multi-view molecular fusion feature that includes molecular fingerprints, molecular descriptors, and molecular energy gaps, which can more comprehensively obtain molecular feature information. The data augmentation module introduces strategies such as SMILES randomization, molecular fingerprint bit-level perturbation, and Gaussian noise injection to enhance the performance of the model in small sample environments. The spectral prediction module captures the complex mapping relationship between molecular structure and spectrum. It is demonstrated that the proposed method provides considerable advantages in the virtual screening of organic fluorescent molecules and offers valuable support for the development of novel BODIPY derivatives based on data-driven strategies.