# Boosted Prediction of Antihypertensive Peptides Using Deep Learning
## 1 Introduction  
Since hypertension (HT) is a general medical issue that affects about twenty-five percent of the populace, the chances of a person becoming hypertensive increase with age [1]. Frequent high blood pressure generally causes hypertension and it is known as a silent killer. HT typically has no apparent symptoms, as compared to other diseases like fever and asthma. Therefore, it might take a while for a person to be diagnosed as hypertensive. Delayed diagnosis may cause severe medical issues like stroke, heart-related diseases, and other significant abnormalities like renal failure, multi-infarct dementia, harm to brain organs, and the cardiovascular illnesses [2, 3]. The high prevalence and dangerous effects of hypertension signify the need to discover novel treatments and drugs to lessen/eradicate its consequences. Currently, there are many drugs for HT available on the market, like angiotensin-converting enzyme (ACE) inhibitors, beta-blockers, calcium channel blockers, k+ sparing diuretics, loop diuretics, and thiazole diuretics [4]. Although these drugs have been proven to be beneficial forthe treatment of HT, they may cause notable side-effects, such as hypotension, metabolic alkalosis, depression, hallucinations, vivid dreams, hyperglycemia, angioneurotic edema, cough, ankle edema, tachycardia, headache, urinary urgency, functional renal insufficiency, hyponatremia, impotence, and insomnia [5]. Therefore, the discovery of secure medicines to treat and/or reduce the harmful effects of hypertension is indispensable. A medicine is considered secure if it has no or minimum side-effects. The angiotensin-converting enzyme (ACE) is an essential element of the renin-angiotensin system (RAS). It is responsible for balancing the fluid volumes in the body and thus controlling the blood pressure. It regulates the conversion of angiotensin-I (a decapeptide) into the active angiotensin-II (an octapeptide), which constricts the blood veins. It is a powerful naphazoline hormone and a mineralocorticoid-animating peptide that controls blood pressure. Thus, ACE is indirectly responsible for high blood pressure by causing blood veins to constrict. Many biological peptides have the potential to inhibit ACE in the renin-angiotensin system, and are thus useful to prevent and treat HT [6]. A large number of such biological peptides are found in the proteins from foods prepared from animals and plants such as, fish, cheese, milk, egg, corn, algae, microorganisms, insects, fungi, wakame, amaranth, soybean, wheat, chicken, snake, bovine, etc. [7]. Such peptides are called antihypertensive peptides (AHTPs), and their identification can lead to the development of more beneficial drugs (with less side effects) for the treatment of HT. However, the identification of a peptide that can function as an antihypertensive peptide is an expensive task in terms of time and cost. In-silico approaches can be of great help in developing systems that can absolutely filter out the non-antihypertensive peptides (non-AHTP) and provide a set of peptides with the potential to inhibit hypertensive conditions. There is a great need to develop such systems that can produce highly accurate predictions of such peptides. Recently, limited studies have demonstrated the power of machine learning (ML)-based methods to develop AHTP prediction systems. Firstly, [PERSON] et al. quantitatively defined the relationship among target molecular structures and biological activities, and created a quantitative structure-activity relationship (QSARs) model to confirm the structure of the ACE-inhibitor peptides and the biological activities of them. To create this model, they utilized g-scale features and used partial least square (PLS) regression-based methods. They have created this model on the basis of very small peptides (e.g., peptides with lengths of two and three), and can only predict the inhibitory activity of these tiny peptides, which is the main disadvantage of this study [8]. Another approach was built by [PERSON] et al. in 2015 in this area [9].