at Google ScholarTitle data
Strothmann, Robert ; Amanpur, Mehran ; Neveselý, Tomáš ; Hecht, Stefan ; Reuter, Karsten ; Margraf, Johannes T.:
Machine learning driven design of spiropyran photoswitches.
In: Digital Discovery.
Vol. 4
(2025)
.
- pp. 3098-3108.
ISSN 2635-098X
DOI: https://doi.org/10.1039/D5DD00327J
Abstract in another language
This study presents the development and application of a generative machine learning model for the design of novel spiropyran photoswitches with enhanced switching speed and absorption bands with small spectral overlap between the open and closed form (i.e. high addressability). Leveraging a scaffold decoration approach, we fine-tuned a general chemical recurrent neural network (RNN) model on a curated dataset of photoswitches. The fine-tuned model was evaluated against both the pretrained baseline and literature-reported spiropyran compounds, demonstrating superior performance in generating diverse and novel candidates. Notably, the fine-tuned model effectively mitigates common biases in decoration patterns and functional group selection observed in the literature. The study also outlines the synthesis and experimental characterization of several newly designed spiropyran photoswitches, validating the design principles derived from the generative model. These findings highlight the potential of generative models in accelerating the discovery of advanced molecular photoswitches with tailored properties.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Institutions of the University: | Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning > Chair Physical Chemistry V - Theory and Machine Learning - Univ.-Prof. Dr. Johannes Theo Margraf Research Institutions > Central research institutes > Research Center for AI in Science and Society |
| Result of work at the UBT: | Yes |
| DDC Subjects: | 500 Science > 540 Chemistry |
| Date Deposited: | 10 Nov 2025 11:43 |
| Last Modified: | 10 Nov 2025 11:43 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/95166 |