Predictive modeling of visible-light azo-photoswitches’ properties using structural features
Abstract In this manuscript we present the strategy for modeling photoswitch properties (maximum absorption wavelength and thermal half-life of photoisomers) of visible-light azo-photoswitches using structural data. We compile a comprehensive data set from literature sources and perform a rigorous b...
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| Language: | English |
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BMC
2025-04-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-025-00993-7 |
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| author | Said Byadi P. K. Hashim Pavel Sidorov |
| author_facet | Said Byadi P. K. Hashim Pavel Sidorov |
| author_sort | Said Byadi |
| collection | DOAJ |
| description | Abstract In this manuscript we present the strategy for modeling photoswitch properties (maximum absorption wavelength and thermal half-life of photoisomers) of visible-light azo-photoswitches using structural data. We compile a comprehensive data set from literature sources and perform a rigorous benchmark to select the best feature type and modeling approach. The fragment counts have demonstrated the best performance in the benchmark for both properties. We validate the models in cross-validation and on an external set. The predictions of absorption wavelengths for this set are highly accurate; on the other hand, the model for thermal half-life is less reliable, likely due to the modest size of the data set related to half-life of photoisomers, although consensus modeling approach allows to improve the predictivity. We also provide an interpretation of the modeling results using ColorAtom approach and the insights into the chemical space covered by the data set. Scientific contribution The paper provides a machine learning approach based only on structural features to predict two important photoswitch properties. Unlike previous studies, we do not use any quantum chemical features which accelerates the modeling procedure, while the accuracy of models remains high. Moreover, the fragment counts offer unique approach to model interpretation that is useful for rational design of photoswitches with desired properties. |
| format | Article |
| id | doaj-art-1cb1f7484e5a409d91145286d6fd641e |
| institution | OA Journals |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-1cb1f7484e5a409d91145286d6fd641e2025-08-20T02:25:41ZengBMCJournal of Cheminformatics1758-29462025-04-011711910.1186/s13321-025-00993-7Predictive modeling of visible-light azo-photoswitches’ properties using structural featuresSaid Byadi0P. K. Hashim1Pavel Sidorov2Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido UniversityResearch Institute for Electronic Science, Hokkaido UniversityInstitute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido UniversityAbstract In this manuscript we present the strategy for modeling photoswitch properties (maximum absorption wavelength and thermal half-life of photoisomers) of visible-light azo-photoswitches using structural data. We compile a comprehensive data set from literature sources and perform a rigorous benchmark to select the best feature type and modeling approach. The fragment counts have demonstrated the best performance in the benchmark for both properties. We validate the models in cross-validation and on an external set. The predictions of absorption wavelengths for this set are highly accurate; on the other hand, the model for thermal half-life is less reliable, likely due to the modest size of the data set related to half-life of photoisomers, although consensus modeling approach allows to improve the predictivity. We also provide an interpretation of the modeling results using ColorAtom approach and the insights into the chemical space covered by the data set. Scientific contribution The paper provides a machine learning approach based only on structural features to predict two important photoswitch properties. Unlike previous studies, we do not use any quantum chemical features which accelerates the modeling procedure, while the accuracy of models remains high. Moreover, the fragment counts offer unique approach to model interpretation that is useful for rational design of photoswitches with desired properties.https://doi.org/10.1186/s13321-025-00993-7PhotoswitchesAzobenzeneQuantitative structure–property relationshipMachine learningMolecular descriptors |
| spellingShingle | Said Byadi P. K. Hashim Pavel Sidorov Predictive modeling of visible-light azo-photoswitches’ properties using structural features Journal of Cheminformatics Photoswitches Azobenzene Quantitative structure–property relationship Machine learning Molecular descriptors |
| title | Predictive modeling of visible-light azo-photoswitches’ properties using structural features |
| title_full | Predictive modeling of visible-light azo-photoswitches’ properties using structural features |
| title_fullStr | Predictive modeling of visible-light azo-photoswitches’ properties using structural features |
| title_full_unstemmed | Predictive modeling of visible-light azo-photoswitches’ properties using structural features |
| title_short | Predictive modeling of visible-light azo-photoswitches’ properties using structural features |
| title_sort | predictive modeling of visible light azo photoswitches properties using structural features |
| topic | Photoswitches Azobenzene Quantitative structure–property relationship Machine learning Molecular descriptors |
| url | https://doi.org/10.1186/s13321-025-00993-7 |
| work_keys_str_mv | AT saidbyadi predictivemodelingofvisiblelightazophotoswitchespropertiesusingstructuralfeatures AT pkhashim predictivemodelingofvisiblelightazophotoswitchespropertiesusingstructuralfeatures AT pavelsidorov predictivemodelingofvisiblelightazophotoswitchespropertiesusingstructuralfeatures |