GR-pKa: a message-passing neural network with retention mechanism for pKa prediction.

ABSTRACT: During the drug discovery and design process, the acid-base dissociation constant (pKa) of a molecule is critically emphasized due to its crucial role in influencing the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties and biological activity. However, the experimental determination of pKa values is often laborious and complex. Moreover, existing prediction methods exhibit limitations in both the quantity and quality of the training data, as well as in their capacity to handle the complex structural and physicochemical properties of compounds, consequently impeding accuracy and generalization. Therefore, developing a method that can quickly and accurately predict molecular pKa values will to some extent help the structural modification of molecules, and thus

SUBMITTER: Miao R 

PROVIDER: S-EPMC11339865 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature