Leveraging graph neural networks to predict Hammett’s constants for benzoic acid derivatives

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The Hammett constants, σm and σp, reflect the electron-withdrawing and electron-donating abilities of substituents on aromatic compounds, and have been successfully used in various structure-activity relationship studies. However, determining these constants experimentally is both resource-intensive and time-consuming approach. In this study, we explore the use of graph neural networks (GNNs) to predict Hammett constant parameters using graph-based features. This innovative approach aims to provide rapid and efficient predictions of σm and σp values, eliminating the need for extensive computational and experimental setups. By leveraging the power of GNNs, we hope to streamline the process of obtaining these critical parameters, thereby facilitating more efficient reaction design and enhancing the applicability of linear free energy relationship studies in chemical research.