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. MA 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 both the message-passing
framework and standard multilayer perceptron neural networks. 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. 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. Finally,
we provide the intuitive analysis between the experimental results
and the chemical meaning of the target.