Data-driven discovery of near-infrared type I photosensitizers for RNA-targeted tumor photodynamic therapy

Type I photodynamic therapy (PDT) offers a promising solution for treating tumors with inherent hypoxia, but developing near-infrared (NIR) type I photosensitizers (PSs) remains a significant challenge. In this work, we propose a data-driven multi-stage screening workflow to rapidly discover novel type I PSs for RNA-targeted tumor PDT. Two custom-built databases were developed: one for training a type I PS identification model (1-PS-GCN) and the other for artificial intelligence (AI)-assisted multi-stage screening. The top 10 candidates with potential type I photosensitizing properties, high synthetic accessibility and RNA-targeting capability were identified. Experts and AI reached a consensus to select the most promising type I PS (PYD). Ultimately, PYD was synthesized and its properties were verified through spectral, cellular, and <i>in vivo</i> PDT experiments. It showed satisfactory phototoxicity and significant tumor inhibition. Our approach establishes an intelligent discovery paradigm to accelerate the identification of NIR type I PSs for advanced PDT.