Bridging Machine Learning and Thermodynamics for Accurate pK _a Prediction.

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 physical chemistry for small organic molecules. Uni-pK _a utilizes a comprehensive free energy model to represent molecular protonation equilibria accurately. It features a structure enumerator that reconstructs molecular configurations f

SUBMITTER: Luo W 

PROVIDER: S-EPMC11423309 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature