{"ID":2837705,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19347","arxiv_id":"2511.19347","title":"Artificial Intelligence Driven Workflow for Accelerating Design of Novel Photosensitizers","abstract":"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).","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2511.19347","url_pdf":"https://arxiv.org/pdf/2511.19347v1","authors":"[\"Hongyi Wang\",\"Xiuli Zheng\",\"Weimin Liu\",\"Zitian Tang\",\"Sheng Gong\"]","published":"2025-11-24T17:46:54Z","proceeding":"cond-mat.mtrl-sci","tasks":"[\"cond-mat.mtrl-sci\",\"cs.LG\",\"physics.chem-ph\"]","methods":"[\"Transformer\"]","has_code":false}