Accelerating the discovery of type II photosensitizer: Experimentally validated machine learning models for predicting the singlet oxygen quantum yield of photosensitive molecule

Photodynamic therapy (PDT) is an emerging cancer treatment that mainly relies on photosensitizer (PS) to generate singlet oxygen for tumor destruction. Developing PSs with high singlet oxygen quantum yields (SO-QYs) requires extensive experimentation, limiting their rapid screening. Herein, to streamline this process, this study introduces several machine learning (ML) models that accurately predicts SO-QY across various experimental conditions. The models' establishment is based on two feature matrices derived from Morgan fingerprints (MFPs) and descriptors (molecular descriptors and quantum chemical descriptors, MD_QCDs). Comparative and evaluative results indicate that the XGBoost model constructed with MFPs and the AdaBoost model constructed with MD_QCDs exhibit superior predictive performance, with R2 values of 0.8648 and 0.8460, respectively. Furthermore, by utilizing SHapley Additive exPlanations (SHAP) analysis and quantum chemistry, we analyzed that the iodine atoms and larger conjugated systems significantly influenced the SO-QY. Experimental validation, based on this analysis, demonstrates that our models not only possess excellent predictive capabilities but also exhibit strong interpretability. In summary, this work has established several interpretable models with outstanding predictive performance, which can aid in the more rapid screening of PSs, thus promoting their application in PDT.