Multi-instance learning of graph neural networks for aqueous pKa prediction.

ABSTRACT:

Motivation

The acid dissociation constant (pKa) is a critical parameter to reflect the ionization ability of chemical compounds and is widely applied in a variety of industries. However, the experimental determination of pKa is intricate and time-consuming, especially for the exact determination of micro-pKa information at the atomic level. Hence, a fast and accurate prediction of pKa values of chemical compounds is of broad interest.

Results

Here, we compiled a large-scale pKa dataset containing 16 595 compounds with 17 489 pKa values. Based on this dataset, a novel pKa prediction model, named Graph-pKa, was established using graph neural networks. Graph-pKa performed well on the prediction of macro-pKa values, with a mean absolute error around 0.55 and a coefficient of det

SUBMITTER: Xiong J 

PROVIDER: S-EPMC8756178 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature