Data-Efficient Active Learning Discovery of Transition Metal Photosensitizers for Type I Photodynamic Therapy
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911531562696704 |
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| author | Fallani, Alessio Haase, Pi A. B. Eckert, Julianne F. F. Nikkanen, Luukas McFarland, Sherri A. Stella, Martina Pavošević, Fabijan |
| author_facet | Fallani, Alessio Haase, Pi A. B. Eckert, Julianne F. F. Nikkanen, Luukas McFarland, Sherri A. Stella, Martina Pavošević, Fabijan |
| contents | Transition-metal complexes (TMCs) are promising photosensitizers for Type~I photodynamic therapy (PDT), where electron-transfer processes can generate reactive oxygen species under hypoxic conditions. Yet identifying candidates with the required ground- and excited-state redox energetics remains challenging across the vast chemical space of TMCs. Here, we develop a data-efficient active learning (AL) framework for the discovery of Type~I active TMC photosensitizers by combining a chemically structured design space of over 2.1 million Ru(II), Os(II), and Ir(III) complexes with targeted DFT calculations and pretrained atomistic representations. With only 300 quantum-chemical evaluations, the approach efficiently enriches candidates within a mechanistically defined optimal redox region. Analysis of the viable complexes reveals chemical design principles linking metal identity, ligand framework, substituent pattern, and physicochemical properties to Type~I photoreactivity, including a pronounced preference for Os(II)-based complexes and electronically asymmetric ligand environments along with combination of electronic donating and accepting substituents. More broadly, the strategy presented herein provides a scalable, mechanism-guided route for the rational design of transition-metal photocatalysts for applications spanning biomedicine, solar energy conversion, and photoredox chemistry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19912 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data-Efficient Active Learning Discovery of Transition Metal Photosensitizers for Type I Photodynamic Therapy Fallani, Alessio Haase, Pi A. B. Eckert, Julianne F. F. Nikkanen, Luukas McFarland, Sherri A. Stella, Martina Pavošević, Fabijan Chemical Physics Transition-metal complexes (TMCs) are promising photosensitizers for Type~I photodynamic therapy (PDT), where electron-transfer processes can generate reactive oxygen species under hypoxic conditions. Yet identifying candidates with the required ground- and excited-state redox energetics remains challenging across the vast chemical space of TMCs. Here, we develop a data-efficient active learning (AL) framework for the discovery of Type~I active TMC photosensitizers by combining a chemically structured design space of over 2.1 million Ru(II), Os(II), and Ir(III) complexes with targeted DFT calculations and pretrained atomistic representations. With only 300 quantum-chemical evaluations, the approach efficiently enriches candidates within a mechanistically defined optimal redox region. Analysis of the viable complexes reveals chemical design principles linking metal identity, ligand framework, substituent pattern, and physicochemical properties to Type~I photoreactivity, including a pronounced preference for Os(II)-based complexes and electronically asymmetric ligand environments along with combination of electronic donating and accepting substituents. More broadly, the strategy presented herein provides a scalable, mechanism-guided route for the rational design of transition-metal photocatalysts for applications spanning biomedicine, solar energy conversion, and photoredox chemistry. |
| title | Data-Efficient Active Learning Discovery of Transition Metal Photosensitizers for Type I Photodynamic Therapy |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2603.19912 |