Lattuada Research Group has recently published an article in the journal ACS Omega, entitled "Comparative Analysis of pKa Predictions for Arsonic Acids Using Density Functional Theory-Based and Machine Learning Approaches".

By integrating quantum chemical calculations with data-driven approaches, this research provides a framework for understanding acid-base equilibria, and supporting pollution control strategies.

For more information and to read the article: https://pubs.acs.org/doi/10.1021/acsomega.4c10413

ABSTRACT

Arsonic acids (RAsO(OH)₂), prevalent in contaminated food, water, air, and soil, pose significant environmental and health risks due to their variable ionization states, which influence key properties such as lipophilicity, solubility, and membrane permeability. Accurate pK_a prediction for these compounds is critical yet challenging, as existing models often exhibit limitations across diverse chemical spaces. This study presents a comparative analysis of pK_a predictions for arsonic acids using a support vector machine-based machine learning (ML) approach and three density functional theory (DFT)-based models. The DFT models evaluated include correlations to the maximum surface electrostatic potential (V_S,max), atomic charges derived from a solvation model (solvation model based on density), and a scaled solvent-accessible surface method.