Heart disease prediction using Machine learning and cardiovascular therapeutics development using molecular intelligence simulations: A perspective review

Abstract

Heart disease prediction using machine learning leverages algorithms to analyze patient data and predict the likelihood of cardiovascular issues. Features like age, cholesterol levels, blood pressure, and lifestyle habits are used to train models such as logistic regression, decision trees, support vector machines, or neural networks. These models identify patterns and risk factors associated with heart disease. Techniques like feature selection enhance model accuracy, while evaluation metrics such as accuracy, precision, and AUC-ROC assess performance. Molecular modeling in cardiovascular drug design utilizes computational techniques to understand and predict the interactions between potential drug molecules and cardiovascular targets. By simulating molecular structures and dynamics, researchers can identify key binding sites on proteins like enzymes or receptors involved in cardiovascular diseases. Methods such as molecular docking, quantum mechanics, and molecular dynamics aid in optimizing drug candidates for efficacy and safety. Virtual screening accelerates the discovery process by evaluating libraries of compounds. The Machine learning aids early diagnosis, enabling timely interventions and personalized treatment plans, ultimately improving patient outcomes and reducing healthcare burdens while the Molecular modeling approaches reduce costs and time in drug development, thus, enabling precise design of therapies targeting conditions like hypertension, atherosclerosis, and heart failure.