Dataset Information

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 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-pKa values.

Availability and implementation

The Graph-pKa 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 | 2022 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Xiong Jiacheng J Li Zhaojun Z Wang Guangchao G Fu Zunyun Z Zhong Feisheng F Xu Tingyang T Liu Xiaomeng X Huang Ziming Z Liu Xiaohong X Chen Kaixian K Jiang Hualiang H Zheng Mingyue M

Bioinformatics (Oxford, England) 20220101 3

<h4>Motivation</h4>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.<h4>Results</h4>Here, we compiled a large-scale pKa da ...[more]

PMID: 34643666

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Dataset Information

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

Motivation

Results

Availability and implementation

Supplementary information

Publications

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

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