PSoSOQY: A Deep Learning–Driven Singlet Oxygen Quantum Yield Prediction Platform for Expediting Photosensitizer Development

The singlet oxygen quantum yield (SOQY) of photosensitizers (PSs) is a critical parameter in photodynamic therapy (PDT), directly linked to therapeutic efficacy by quantifying singlet oxygen generation. Traditional approaches to developing high‐SOQY PSs are complex, involving elaborate molecular design and challenging SOQY measurements, which hinder rapid therapeutic advancement. This study constructs a comprehensive dataset to develop BiLSTM + attention (BA)‐SOQY, a cutting‐edge prediction model integrating bidirectional long short‐term memory (BiLSTM) networks and attention mechanisms. BiLSTM captures long‐term dependencies in sequential data, while the attention mechanism highlights critical input features, enabling efficient screening of potential PSs candidates. On the held‐out test set, BA‐SOQY achieves R 2 = 0.9140; validations on ESOL and FreeSolv datasets yield R 2 > 0.9, supporting robustness and generalization. Additionally, substructure masking interpretation strategy based on SMILES (SMIS‐SMILES) is introduced. Combined with BA‐SOQY, this forms the PSoSOQY platform, which not only provides accurate SOQY predictions but also reveals how substructures influence SOQY. These insights accelerate rational PSs design and high‐throughput screening, driving progress in PDT research.