A Deep Dive into Machine Learning: The Roles of Neural Networks and Random Forests in QSPR Analysis

Machine learning has significantly improved the field of drug development by enabling the accurate prediction of physicochemical properties and biological activities of compounds. Using machine learning and topological indices to analyze a drug's structures can make process faster and more accurate. Our study explores the molecular characteristics of 15 sulfur-based drugs (SVI)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(S<^>{VI})$$\end{document}. Topological indices of these drugs have been calculated, and physiochemical properties have been examined using machine learning algorithms. Machine learning algorithms such as artificial neural networks, random forests, and adaptive boosting play a crucial role in this process. These algorithms utilize labeled data to make predictions about intricate molecular activities by assisting in the discovery of novel medication candidates and the enhancement of their properties. These algorithms enhance the accuracy of predictions related to physiochemical properties, reduce the time and cost associated with drug discovery, and rapidly analyze vast datasets by utilizing machine learning, consequently expediting the advancement of novel and efficient therapies.