Artificial Intelligence Driven Workflow for Accelerating Design of Novel Photosensitizers
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909921466908672 |
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| author | Wang, Hongyi Zheng, Xiuli Liu, Weimin Tang, Zitian Gong, Sheng |
| author_facet | Wang, Hongyi Zheng, Xiuli Liu, Weimin Tang, Zitian Gong, Sheng |
| contents | 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). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_19347 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence Driven Workflow for Accelerating Design of Novel Photosensitizers Wang, Hongyi Zheng, Xiuli Liu, Weimin Tang, Zitian Gong, Sheng Materials Science Machine Learning Chemical Physics 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). |
| title | Artificial Intelligence Driven Workflow for Accelerating Design of Novel Photosensitizers |
| topic | Materials Science Machine Learning Chemical Physics |
| url | https://arxiv.org/abs/2511.19347 |