Computational-Chemistry-Based Prediction of Near-Infrared Rhodamine Fluorescence Peaks with Sub-12 nm Accuracy
Near-infrared (NIR) rhodamine dyes are pivotal for bioimaging due to the minimal tissue interference. Yet, their rational design is hindered by unreliable computational methods for excited-state property prediction. We benchmarked the time-dependent density functional theory (TDDFT) with the linear-...
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MDPI AG
2025-06-01
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| Series: | Photochem |
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| Online Access: | https://www.mdpi.com/2673-7256/5/2/15 |
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| author | Qinlin Yuan Hanwei Wang Pingping Sun Chaoyuan Zeng Weijie Chi |
| author_facet | Qinlin Yuan Hanwei Wang Pingping Sun Chaoyuan Zeng Weijie Chi |
| author_sort | Qinlin Yuan |
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| description | Near-infrared (NIR) rhodamine dyes are pivotal for bioimaging due to the minimal tissue interference. Yet, their rational design is hindered by unreliable computational methods for excited-state property prediction. We benchmarked the time-dependent density functional theory (TDDFT) with the linear-response (LR) and state-specific (SS) solvation models across five functionals (CAM-B3LYP, M06-2X, ωB97X-D, B3LYP, MN15) and optimized the ground/excited states for 42 rhodamine derivatives. A robust linear calibration framework was established by connecting the computed and experimental wavelengths, which was rigorously validated through six-fold cross-validation. The key metrics included the mean absolute error (MAE) and R<sup>2</sup> to assess the prediction robustness. CAM-B3LYP combined with LR solvation achieved the highest accuracy (absorption: MAE = 6 nm, R<sup>2</sup> = 0.94; emission: MAE = 12 nm, R<sup>2</sup> = 0.72). By integrating the TDDFT with a calibrated linear-response solvation model, we achieved sub-12 nm accuracy in predicting the NIR fluorescence peaks. This framework enabled the rational design of nine novel rhodamine derivatives with emissions beyond 700 nm, offering a paradigm shift in bioimaging probe development. |
| format | Article |
| id | doaj-art-7b2b30e84c644d81862fe93da52fe8f8 |
| institution | Kabale University |
| issn | 2673-7256 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Photochem |
| spelling | doaj-art-7b2b30e84c644d81862fe93da52fe8f82025-08-20T03:27:36ZengMDPI AGPhotochem2673-72562025-06-01521510.3390/photochem5020015Computational-Chemistry-Based Prediction of Near-Infrared Rhodamine Fluorescence Peaks with Sub-12 nm AccuracyQinlin Yuan0Hanwei Wang1Pingping Sun2Chaoyuan Zeng3Weijie Chi4School of Chemistry and Chemical Engineering, Hainan University, Haikou 570228, ChinaSchool of Chemistry and Chemical Engineering, Hainan University, Haikou 570228, ChinaSchool of Chemistry and Chemical Engineering, Hainan University, Haikou 570228, ChinaSchool of Chemistry and Chemical Engineering, Hainan University, Haikou 570228, ChinaSchool of Chemistry and Chemical Engineering, Hainan University, Haikou 570228, ChinaNear-infrared (NIR) rhodamine dyes are pivotal for bioimaging due to the minimal tissue interference. Yet, their rational design is hindered by unreliable computational methods for excited-state property prediction. We benchmarked the time-dependent density functional theory (TDDFT) with the linear-response (LR) and state-specific (SS) solvation models across five functionals (CAM-B3LYP, M06-2X, ωB97X-D, B3LYP, MN15) and optimized the ground/excited states for 42 rhodamine derivatives. A robust linear calibration framework was established by connecting the computed and experimental wavelengths, which was rigorously validated through six-fold cross-validation. The key metrics included the mean absolute error (MAE) and R<sup>2</sup> to assess the prediction robustness. CAM-B3LYP combined with LR solvation achieved the highest accuracy (absorption: MAE = 6 nm, R<sup>2</sup> = 0.94; emission: MAE = 12 nm, R<sup>2</sup> = 0.72). By integrating the TDDFT with a calibrated linear-response solvation model, we achieved sub-12 nm accuracy in predicting the NIR fluorescence peaks. This framework enabled the rational design of nine novel rhodamine derivatives with emissions beyond 700 nm, offering a paradigm shift in bioimaging probe development.https://www.mdpi.com/2673-7256/5/2/15rhodaminecomputational chemistrynear-infrared IIfluorescence wavelength |
| spellingShingle | Qinlin Yuan Hanwei Wang Pingping Sun Chaoyuan Zeng Weijie Chi Computational-Chemistry-Based Prediction of Near-Infrared Rhodamine Fluorescence Peaks with Sub-12 nm Accuracy Photochem rhodamine computational chemistry near-infrared II fluorescence wavelength |
| title | Computational-Chemistry-Based Prediction of Near-Infrared Rhodamine Fluorescence Peaks with Sub-12 nm Accuracy |
| title_full | Computational-Chemistry-Based Prediction of Near-Infrared Rhodamine Fluorescence Peaks with Sub-12 nm Accuracy |
| title_fullStr | Computational-Chemistry-Based Prediction of Near-Infrared Rhodamine Fluorescence Peaks with Sub-12 nm Accuracy |
| title_full_unstemmed | Computational-Chemistry-Based Prediction of Near-Infrared Rhodamine Fluorescence Peaks with Sub-12 nm Accuracy |
| title_short | Computational-Chemistry-Based Prediction of Near-Infrared Rhodamine Fluorescence Peaks with Sub-12 nm Accuracy |
| title_sort | computational chemistry based prediction of near infrared rhodamine fluorescence peaks with sub 12 nm accuracy |
| topic | rhodamine computational chemistry near-infrared II fluorescence wavelength |
| url | https://www.mdpi.com/2673-7256/5/2/15 |
| work_keys_str_mv | AT qinlinyuan computationalchemistrybasedpredictionofnearinfraredrhodaminefluorescencepeakswithsub12nmaccuracy AT hanweiwang computationalchemistrybasedpredictionofnearinfraredrhodaminefluorescencepeakswithsub12nmaccuracy AT pingpingsun computationalchemistrybasedpredictionofnearinfraredrhodaminefluorescencepeakswithsub12nmaccuracy AT chaoyuanzeng computationalchemistrybasedpredictionofnearinfraredrhodaminefluorescencepeakswithsub12nmaccuracy AT weijiechi computationalchemistrybasedpredictionofnearinfraredrhodaminefluorescencepeakswithsub12nmaccuracy |