Open AccessMulti-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) - computer science , graph , artificial neural network , dissociation constant , acid dissociation constant , aqueous solution , artificial intelligence , machine learning , chemistry , theoretical computer science , biochemistry , receptor
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.