Machine Learning-aided Computational Fragment-based Design of Small Molecules for Hypertension Treatment

Abstract

With over 1 billion affected adults, hypertension is one of the most critical public health challenges worldwide. If left untreated over time, hypertension increases the likelihood of premature disability or death from cardiovascular diseases. Despite the range of medications available for the treatment of hypertension, many individuals do not respond positively to the treatment. Additionally, a significant percentage of the population does not take the medication as prescribed, which is sometimes attributed to intolerable side effects. Hence, there is still the need to develop new hypertension drugs that provide patients with favourable treatment outcomes. This paper explores the computational method of drug discovery to generate new lead drug molecules for hypertension by targeting the renin-angiotensin-aldosterone system (RAAS). Specifically, we proposed a framework that integrates computational fragment-based methods and an unsupervised machine learning technique to generate new lead Angiotensin-Converting Enzyme Inhibitor (ACEI) and Angiotensin-Receptor Blocker (ARB) molecules. The molecule generation process is initiated using all the approved agents acting on the RAAS that are available in the ChEMBL and DrugBank databases to create a fragment pool. The fragments are used to generate new molecules, which are categorised into ACEI and ARB clusters using unsupervised machine learning techniques. The generated molecules in each category are screened to determine their suitability as oral drug molecules, considering their physicochemical properties. Further screening is performed to determine the molecules’ suitability as ACEIs or ARBs, based on the presence of the appropriate functional groups and their similarities with existing drug molecules. The resultant molecules that passed screening are proposed as new lead antihypertensive agents. A synthesizability test is also performed on the final new lead molecules to determine the ease of making them compared to the original molecules.