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

During the drug discovery and design process, the acid-base dissociation constant (pK(a)) 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 pK(a) 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 pK(a) values will to some extent help the structural modification of molecules, and thus assist the development process of new drugs. In this study, we developed a cutting-edge pK(a) prediction model named GR-pK(a) (Graph Retention pK(a)), leveraging a message-passing neural network and employing a multi-fidelity learning strategy to accurately predict molecular pK(a) values. The GR-pK(a) model incorporates five quantum mechanical properties related to molecular thermodynamics and dynamics as key features to characterize molecules. Notably, we originally introduced the novel retention mechanism into the message-passing phase, which significantly improves the model's ability to capture and update molecular information. Our GR-pK(a) model outperforms several state-of-the-art models in predicting macro-pK(a) values, achieving impressive results with a low mean absolute error of 0.490 and root mean square error of 0.588, and a high R-2 of 0.937 on the SAMPL7 dataset.