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

The study sought to develop a machine learning-aided computational drug discovery system to generate new lead drug molecules for hypertension treatment by targeting the renin-angiotensin-aldosterone system (RAAS).
The main agents that act on the RAAS are commonly classified as Angiotensin-Converting Enzyme Inhibitors (ACEIs) or Angiotensin II Receptor Blockers (ARBs), therefore, the objective was to generate new lead ACEIs and ARBs to treat hypertension through the RAAS.

As a result, we developed a seven (7) phase computational fragment-based drug design system aided by machine learning, which guides the process of using existing hypertension molecules as the basis for discovering new hypertension lead (candidate) molecules.

The output of this study was a dataset of newly generated lead Angiotensin-Converting Enzyme Inhibitor (ACEI) and Angiotensin II Receptor Blocker (ARB) molecules.

The Input Data folder below contains all the files that were used to generate this dataset, which can be found in the Output Data folder below.