Deep learning-based discovery of compounds for blood pressure lowering effects
Abstract The hypotensive side effects caused by drugs during their use have been a vexing issue. Recent studies have found that deep learning can effectively predict the biological activity of compounds by mining patterns and rules in the data, providing a potential solution for identifying drug sid...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83924-0 |
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| author | Rongzhen Li Tianchi Wu Xiaotian Xu Xiaoqun Duan Yuhui Wang |
| author_facet | Rongzhen Li Tianchi Wu Xiaotian Xu Xiaoqun Duan Yuhui Wang |
| author_sort | Rongzhen Li |
| collection | DOAJ |
| description | Abstract The hypotensive side effects caused by drugs during their use have been a vexing issue. Recent studies have found that deep learning can effectively predict the biological activity of compounds by mining patterns and rules in the data, providing a potential solution for identifying drug side effects. In this study, we established a deep learning-based predictive model, utilizing a data set comprised of compounds known to either elevate or lower blood pressure. Subsequently, the trained model was used to predict the blood pressure-lowering effects of 26,000 compounds. Based on the predicted results, we randomly selected 50 molecules for validation and compared them with literature reports. The results showed that the predictions for 30 molecules were consistent with literature reports, with known antihypertensive drugs such as reserpine, guanethidine, and mecamylamine ranking at the top. We further selected 10 of these molecules and 3 related protein targets for molecular docking, and the docking results indirectly confirmed the model’s accuracy. Ultimately, we discovered and validated that salaprinol significantly inhibits ACE1 activity and lowers canine blood pressure. In summary, we have established a highly accurate activity prediction model and confirmed its accuracy in predicting potential blood pressure-lowering compounds, which is expected to help patients avoid hypotensive side effects during clinical medication and also provide significant assistance in the discovery of antihypertensive drugs. |
| format | Article |
| id | doaj-art-f591f84d58674640974db8b3981bed66 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f591f84d58674640974db8b3981bed662025-01-05T12:19:04ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-024-83924-0Deep learning-based discovery of compounds for blood pressure lowering effectsRongzhen Li0Tianchi Wu1Xiaotian Xu2Xiaoqun Duan3Yuhui Wang4School of Pharmacy, Guilin Medical UniversitySchool of Pharmacy, Guilin Medical UniversitySchool of Pharmacy, Guilin Medical UniversitySchool of Pharmacy, Guilin Medical UniversitySchool of Pharmacy, Guilin Medical UniversityAbstract The hypotensive side effects caused by drugs during their use have been a vexing issue. Recent studies have found that deep learning can effectively predict the biological activity of compounds by mining patterns and rules in the data, providing a potential solution for identifying drug side effects. In this study, we established a deep learning-based predictive model, utilizing a data set comprised of compounds known to either elevate or lower blood pressure. Subsequently, the trained model was used to predict the blood pressure-lowering effects of 26,000 compounds. Based on the predicted results, we randomly selected 50 molecules for validation and compared them with literature reports. The results showed that the predictions for 30 molecules were consistent with literature reports, with known antihypertensive drugs such as reserpine, guanethidine, and mecamylamine ranking at the top. We further selected 10 of these molecules and 3 related protein targets for molecular docking, and the docking results indirectly confirmed the model’s accuracy. Ultimately, we discovered and validated that salaprinol significantly inhibits ACE1 activity and lowers canine blood pressure. In summary, we have established a highly accurate activity prediction model and confirmed its accuracy in predicting potential blood pressure-lowering compounds, which is expected to help patients avoid hypotensive side effects during clinical medication and also provide significant assistance in the discovery of antihypertensive drugs.https://doi.org/10.1038/s41598-024-83924-0Deep learningHypotensionMolecules predictionMolecular dockingRDKit |
| spellingShingle | Rongzhen Li Tianchi Wu Xiaotian Xu Xiaoqun Duan Yuhui Wang Deep learning-based discovery of compounds for blood pressure lowering effects Scientific Reports Deep learning Hypotension Molecules prediction Molecular docking RDKit |
| title | Deep learning-based discovery of compounds for blood pressure lowering effects |
| title_full | Deep learning-based discovery of compounds for blood pressure lowering effects |
| title_fullStr | Deep learning-based discovery of compounds for blood pressure lowering effects |
| title_full_unstemmed | Deep learning-based discovery of compounds for blood pressure lowering effects |
| title_short | Deep learning-based discovery of compounds for blood pressure lowering effects |
| title_sort | deep learning based discovery of compounds for blood pressure lowering effects |
| topic | Deep learning Hypotension Molecules prediction Molecular docking RDKit |
| url | https://doi.org/10.1038/s41598-024-83924-0 |
| work_keys_str_mv | AT rongzhenli deeplearningbaseddiscoveryofcompoundsforbloodpressureloweringeffects AT tianchiwu deeplearningbaseddiscoveryofcompoundsforbloodpressureloweringeffects AT xiaotianxu deeplearningbaseddiscoveryofcompoundsforbloodpressureloweringeffects AT xiaoqunduan deeplearningbaseddiscoveryofcompoundsforbloodpressureloweringeffects AT yuhuiwang deeplearningbaseddiscoveryofcompoundsforbloodpressureloweringeffects |