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

Abstract

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 in vivo 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.