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

Jiacheng Xiong | Zendy; Zhaojun Li | Zendy; Guangchao Wang | Zendy; Zunyun Fu | Zendy; Feisheng Zhong | Zendy; Tingyang Xu | Zendy; Xiaomeng Liu | Zendy; Ziming Huang | Zendy; Xiaohong Liu | Zendy; Kaixian Chen | Zendy; Hualiang Jiang | Zendy; Mingyue Zheng | Zendy

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

Author(s) -

Jiacheng Xiong,

Zhaojun Li,

Guangchao Wang,

Zunyun Fu,

Feisheng Zhong,

Tingyang Xu,

Xiaomeng Liu,

Ziming Huang,

Xiaohong Liu,

Kaixian Chen,

Hualiang Jiang,

Mingyue Zheng

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab714

Subject(s) - artificial neural network , computer science , graph , artificial intelligence , machine learning , aqueous solution , theoretical computer science , chemistry , organic chemistry

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.

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