  A molecule is a complex of heterogeneous components, and the spatial
arrangements of these components determine the whole molecular properties and
characteristics. With the advent of deep learning in computational chemistry,
several studies have focused on how to predict molecular properties based on
molecular configurations. Message passing neural network provides an effective
framework for capturing molecular geometric features with the perspective of a
molecule as a graph. However, most of these studies assumed that all
heterogeneous molecular features, such as atomic charge, bond length, or other
geometric features always contribute equivalently to the target prediction,
regardless of the task type. In this study, we propose a dual-branched neural
network for molecular property prediction based on message-passing framework.
Our model learns heterogeneous molecular features with different scales, which
are trained flexibly according to each prediction target. In addition, we
introduce a discrete branch to learn single atom features without local
aggregation, apart from message-passing steps. We verify that this novel
structure can improve the model performance with faster convergence in most
targets. The proposed model outperforms other recent models with sparser
representations. Our experimental results indicate that in the chemical
property prediction tasks, the diverse chemical nature of targets should be
carefully considered for both model performance and generalizability.
