  Machine learning is often used in virtual screening to find compounds that
are pharmacologically active on a target protein. The weave module is a type of
graph convolutional deep neural network that uses not only features focusing on
atoms alone (atom features) but also features focusing on atom pairs (pair
features); thus, it can consider information of nonadjacent atoms. However, the
correlation between the distance on the graph and the three-dimensional
coordinate distance is uncertain. In this paper, we propose three improvements
for modifying the weave module. First, the distances between ring atoms on the
graph were modified to bring the distances on the graph closer to the
coordinate distance. Second, different weight matrices were used depending on
the distance on the graph in the convolution layers of the pair features.
Finally, a weighted sum, by distance, was used when converting pair features to
atom features. The experimental results show that the performance of the
proposed method is slightly better than that of the weave module, and the
improvement in the distance representation might be useful for compound
activity prediction.
