Thet Su WinNalini SchaduangratVirapong PrachayasittikulChanin NantasenamatWatshara ShoombuatongMahidol UniversityUniversity of Medical Technology2019-08-232019-08-232018-01-01Future Medicinal Chemistry. Vol.10, No.15 (2018), 1749-176717568927175689192-s2.0-85051583109https://repository.li.mahidol.ac.th/handle/20.500.14594/45287© 2018 Newlands Press. Aim: Hypertension is associated with development of cardiovascular disease and has become a significant health problem worldwide. Naturally-derived antihypertensive peptides have emerged as promising alternatives to synthetic drugs. Materials & methods: This study introduces predictor of antihypertensive activity of peptides constructed using random forest classifier as a function of various combinations of amino acid, dipeptide and pseudoamino acid composition descriptors. Results: Classification models were assessed via independent test set that demonstrated accuracy of 84.73%. Feature importance analysis revealed the preference of proline and hydrophobic amino acids at the C-terminal as well as the preference of short peptides for robust activity. Conclusion: Model presented herein serves as a useful tool for predicting and analysis of antihypertensive activity of peptides.Mahidol UniversityBiochemistry, Genetics and Molecular BiologyPharmacology, Toxicology and PharmaceuticsArticleSCOPUS10.4155/fmc-2017-0300