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.
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 dualbranched neural network for molecular property prediction based on message-passing framework. Our model learns heterogeneous molecular features with different scales,
Recently, graph neural networks (GNNs) have achieved remarkable performances for quantum mechanical problems. However, a graph convolution can only cover a localized region, and cannot capture long-range interactions of atoms. This behavior is contrary to theoretical interatomic potentials, which is a fundamental limitation of the spatial based GNNs. In this work, we propose a novel attentionbased framework for molecular property prediction tasks. We represent a molecular conformation as a discrete atomic sequence combined by atom-atom distance attributes, named Geometry-aware Transformer (GeoT). In particular, we adopt a Transformer architecture, which has been widely used for sequential data. Our proposed model trains sequential representations of molecular graphs based on globally constructed attentions, maintaining all spatial arrangements of atom pairs. Our method does not suffer from cost intensive computations, such as angle calculations. The experimental results on several public benchmarks and visualization maps verified that keeping the long-range interatomic attributes can significantly improve the model predictability.Preprint. Under review.
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