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QupKake: Integrating Machine Learning and Quantum Chemistry for Micro-pK_a Predictions.

ABSTRACT: Accurate prediction of micro-pK_a values is crucial for understanding and modulating the acidity and basicity of organic molecules, with applications in drug discovery, materials science, and environmental chemistry. This work introduces QupKake, a novel method that combines graph neural network models with semiempirical quantum mechanical (QM) features to achieve exceptional accuracy and generalization in micro-pK_a prediction. QupKake outperforms state-of-the-art models on a variety of benchmark data sets, with root-mean-square errors between 0.5 and 0.8 pK_a units on five external test sets. Feature importance analysis reveals the crucial role of QM features in both the reaction site enumeration and micro-pK_a prediction models. QupKake represents a significant advancement in micro-pK_a prediction, offering a powerful tool for various applications in chemistry and beyond.

SUBMITTER: Abarbanel OD

PROVIDER: S-EPMC11325546 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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QupKake: Integrating Machine Learning and Quantum Chemistry for Micro-pKa Predictions.

Abarbanel Omri D OD Hutchison Geoffrey R GR

Journal of chemical theory and computation 20240604 15

Accurate prediction of micro-pKa values is crucial for understanding and modulating the acidity and basicity of organic molecules, with applications in drug discovery, materials science, and environmental chemistry. This work introduces QupKake, a novel method that combines graph neural network models with semiempirical quantum mechanical (QM) features to achieve exceptional accuracy and generalization in micro-pKa prediction. QupKake outperforms state-of-the- ...[more]

PMID: 38832803

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