  Scalar coupling constant (SCC) plays a key role in the analysis of
three-dimensional structure of organic matter, however, the traditional SCC
prediction using quantum mechanical calculations is very time-consuming. To
calculate SCC efficiently and accurately, we proposed a graph embedding local
self-attention encoder (GELAE) model, in which, a novel invariant structure
representation of the coupling system in terms of bond length, bond angle and
dihedral angle was presented firstly, and then a local self-attention module
embedded with the adjacent matrix of a graph was designed to extract
effectively the features of coupling systems, finally, with a modified
classification loss function, the SCC was predicted. To validate the
superiority of the proposed method, we conducted a series of comparison
experiments using different structure representations, different attention
modules, and different losses. The experimental results demonstrate that,
compared to the traditional chemical bond structure representations, the
rotation and translation invariant structure representations proposed in this
work can improve the SCC prediction accuracy; with the graph embedded local
self-attention, the mean absolute error (MAE) of the prediction model in the
validation set decreases from 0.1603 Hz to 0.1067 Hz; using the classification
based loss function instead of the scaled regression loss, the MAE of the
predicted SCC can be decreased to 0.0963 HZ, which is close to the quantum
chemistry standard on CHAMPS dataset.
