2025/11/30 by Kaihang Huang, Yubiao Yang, Huahua Huang +5 · 1 voice
Engineering · Materials Science · Medicine · #Nanoplatforms for cancer theranostics #Photodynamic Therapy Research Studies #Porphyrin and Phthalocyanine Chemistry
paper · doi:10.1021/acs.jpclett.5c03041
openalex created_date 2025/11/30 · openalex publication_date 2025/11/30 · openalex updated_date 2026/07/30
This study centers on corrole, an emerging photosensitizer with great application potential, and innovatively develops an intelligent machine learning-based screening strategy. Through integrating molecular descriptor generation, feature engineering, multiple predictive models, and SHapley Additive exPlanations (SHAP)-based feature analysis, we have predicted two key performances of corrole photosensitizers, involving absorption peak wavelength and singlet–triplet intersystem crossing rate ( k ISC ). Among 10 tested models, XGBoost outperforms other models. After introducing descriptors such as electronic structure and excited-state characteristics and conducting dimensionality reduction, its coefficient of determination is up to 0.87 for k ISC prediction. SHAP analysis clarifies core design principles such as flat molecular structure, HOMO localization/LUMO delocalization and low surface electrostatic potential. The screened corrole with peripheral pyridyls, central nonmetallic P, and hydroxyls has excellent red light/NIR optical performance. Upon light irradiations, it exhibits a robust capacity for generating reactive oxygen species and enables the complete inactivation of cancer cells in vitro and in vivo . By leveraging fluorescence lifetime imaging, this corrole enables effective discrimination between normal cells and cancer cells. The intelligent screening strategy offers a novel paradigm for the efficient development of high-performance photosensitizers with distinct clinical translation potential.