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Deep learning-based discovery of compounds for blood pressure lowering effects.

Authors :
Li R
Wu T
Xu X
Duan X
Wang Y
Source :
Scientific reports [Sci Rep] 2025 Jan 02; Vol. 15 (1), pp. 54. Date of Electronic Publication: 2025 Jan 02.
Publication Year :
2025

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.<br />Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Institutional review board statement: This study was approved by the Animal Ethics Commit-tee of Guilin Medical University, and the number is GLMC202103003. The study is reported in accordance with ARRIVE guidelines ( https://arriveguidelines.org ).<br /> (© 2024. The Author(s).)

Details

Language :
English
ISSN :
2045-2322
Volume :
15
Issue :
1
Database :
MEDLINE
Journal :
Scientific reports
Publication Type :
Academic Journal
Accession number :
39747442
Full Text :
https://doi.org/10.1038/s41598-024-83924-0