Bridging Machine Learning and Thermodynamics for Accurate p <i>K</i> <sub>a</sub> Prediction
Weiliang Luo, Gengmo Zhou, Zhengdan Zhu, Yannan Yuan, Guolin Ke, Zhewei Wei, Zhifeng Gao, Hang Zheng
Abstract
High Resolution Image Download MS PowerPoint Slide Integrating scientific principles into machine learning models to enhance their predictive performance and generalizability is a central challenge in the development of AI for Science. Herein, we introduce Uni-p K a, a novel framework that successfully incorporates thermodynamic principles into machine learning modeling, achieving high-precision predictions of acid dissociation constants (p K a ), a crucial task in the rational design of drugs and catalysts, as well as a modeling challenge in computational physical chemistry for small organic molecules. Uni-p K a utilizes a comprehensive free energy model to represent molecular protonation equilibria accurately. It features a structure enumerator that reconstructs molecular configurations from p K a data, coupled with a neural network that functions as a free energy predictor, ensuring high-throughput, data-driven prediction while preserving thermodynamic consistency. Employing a pretraining-finetuning strategy with both predicted and experimental p K a data, Uni-p K a not only achieves state-of-the-art accuracy in chemoinformatics but also shows comparable precision to quantum mechanics-based methods.