Machine learning for characterizing halogen bonding interactions

Abstract

Halogen bonding, a non-covalent interaction involving halogen atoms as electron acceptors, plays a crucial role in various chemical and biological processes. Understanding the subtleties of halogen bonding interactions is essential for advancing fields such as drug design, materials science, and supramolecular chemistry. This article explores the application of machine learning techniques in characterizing halogen bonding interactions, providing a novel and efficient approach to deciphering complex molecular relationships. Machine learning models, ranging from classical methods to advanced deep learning architectures, are employed to analyze large datasets of molecular structures and their corresponding halogen bonding properties. These models are trained on diverse sets of halogen-containing compounds, enabling them to recognize patterns and correlations that may be challenging for traditional computational methods. The article discusses the potential of machine learning in predicting halogen bond strength, identifying key molecular features influencing interaction strength, and elucidating structure-activity relationships. The presented findings underscore the potential of machine learning as a powerful tool for unravelling the intricacies of molecular interactions and guiding the design of functional molecular systems.