A Closed‐Loop Hybrid Discovery System of Type I Photosensitizers for Hypoxic Tumor Therapy

Type I photosensitizers (PSs), which operate effectively under low-oxygen conditions, offer a promising approach to overcome hypoxia-associated challenges in solid tumor therapy. However, their design remains challenging due to the limited number of reported molecules with diverse structures, as well as insufficient understanding of the underlying mechanisms. Herein, a closed-loop hybrid discovery system is developed that combines molecular excited-state calculations with machine learning (ML) to rationally design and predict high-performance Type I PSs for hypoxic tumor therapy. Through a support vector machine (SVM) classification model, 664 potential Type I PSs are identified from a molecular space based on donor-acceptor (D-A) and donor-acceptor-donor (D-A-D) structures. Among these, two candidates, M1 and M2, are synthesized and experimentally verified as Type I PSs, exhibiting aggregate-induced enhancement of Type I reactive oxygen species (ROS) generation. Both in vitro and in vivo studies demonstrated their ability to induce intracellular Type I ROS generation and effectively suppress tumor growth. The work highlights the potential of ML in the design and prediction of Type I PSs for hypoxic tumor therapy.