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 16595 compounds with 17489 pKa values. Based on this dataset, a novel pK a prediction model, named Graph-pKa, was established using graph neural networks. Graph-pKa performed well on the prediction of macro pK a values, with a mean absolute error around 0.55 and a coefficient of determination around 0.92 on the test dataset. Furthermore, combining multi-instance learning, Graph-pKa was also able to automatically deconvolute the predicted macro pKa into discrete micro pK a values.

Availability

The Graph-pK a model is now freely accessible via a web-based interface (https://pka.simm.ac.cn/).

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Xiong J 

PROVIDER: S-EPMC8756178 | biostudies-literature |

REPOSITORIES: biostudies-literature