Bridging Machine Learning and Thermodynamics for Accurate pKa Prediction
Weiliang Luo,
Gengmo Zhou,
Zhengdan Zhu
et al.
Abstract: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-pK a , a novel framework that successfully incorporates thermodynamic principles into machine learning modeling, achieving high-precision predictions of acid dissociation constants (pK a ), a crucial task in the rational design of drugs and catalysts, as well as a modeling challenge in computational phys… Show more
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