AI驱动流程加速新型光敏剂设计，实现高效高产。
Artificial Intelligence Driven Workflow for Accelerating Design of Novel Photosensitizers
- 结合专家知识与贝叶斯优化，闭环生成新结构光敏剂。
- 筛选出6148个可合成候选物，其中HB4Ph量子产率0.85、吸收波长650nm。
- 适合药物研发与材料设计人员快速发现高性能光敏剂。
高性能光敏剂的发现长期受限于传统试错方法耗时耗力的问题。本文提出AI加速光敏剂创新（AAPSI）的闭环工作流，融合专家知识、基于骨架的分子生成和贝叶斯优化，加速新型光敏剂的设计。AAPSI通过骨架驱动生成确保结构新颖性和可合成性，迭代的AI-实验循环提升发现效率。该流程利用包含102,534个光敏剂-溶剂对的定制数据库，生成6,148个可合成候选物，并通过图神经网络预测单线态氧量子产率（ϕ_Δ）和最大吸收波长（λ_max），经实验验证。本研究发现多个新型候选物，其中基于红紫素的候选物HB4Ph在单线态氧量子产率高且吸收波长长方面达到当前光敏剂的帕累托前沿（ϕ_Δ=0.85，λ_max=650nm），适用于光动力治疗。
原文摘要 · Abstract (English)
The discovery of high-performance photosensitizers has long been hindered by the time-consuming and resource-intensive nature of traditional trial-and-error approaches. Here, we present \textbf{A}I-\textbf{A}ccelerated \textbf{P}hoto\textbf{S}ensitizer \textbf{I}nnovation (AAPSI), a closed-loop workflow that integrates expert knowledge, scaffold-based molecule generation, and Bayesian optimization to accelerate the design of novel photosensitizers. The scaffold-driven generation in AAPSI ensures structural novelty and synthetic feasibility, while the iterative AI-experiment loop accelerates the discovery of novel photosensitizers. AAPSI leverages a curated database of 102,534 photosensitizer-solvent pairs and generate 6,148 synthetically accessible candidates. These candidates are screened via graph transformers trained to predict singlet oxygen quantum yield ($ϕ_Δ$) and absorption maxima ($λ_{max}$), following experimental validation. This work generates several novel candidates for photodynamic therapy (PDT), among which the hypocrellin-based candidate HB4Ph exhibits exceptional performance at the Pareto frontier of high quantum yield of singlet oxygen and long absorption maxima among current photosensitizers ($ϕ_Δ$=0.85, $λ_{max}$=650nm).
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