  We proposed the molecular hyper-message passing network (MolHMPN) that
predicts the properties of a molecule with prior knowledge-guided subgraph.
Modeling higher-order connectivities in molecules is necessary as changes in
both the pair-wise and higher-order interactions among atoms results in the
change of molecular properties. Many approaches have attempted to model the
higher-order connectivities. However, those methods relied heavily on
data-driven approaches, and it is difficult to determine if the utilized
subgraphs contain any properties of interest or have any significance on the
molecular properties. Hence, we propose MolHMPN to utilize the functional group
prior knowledge and model the pair-wise and higher-order connectivities among
the atoms in a molecule. Molecules can contain many types of functional groups,
which affect the properties the molecules. For example, the toxicity of a
molecule is associated with toxicophores, such as nitroaromatic groups and
thiourea. MolHMPN uses functional groups to construct hypergraphs, modifies the
hypergraph using domain knowledge-guided modification scheme, embeds the graph
and hypergraph inputs using a hypergraph message passing (HyperMP) layer, and
uses the updated graph and hypergraph embeddings to predict the properties of
the molecules. Our model provides a way to utilize prior knowledge in chemistry
for molecular properties prediction tasks, and balance between the usage of
prior knowledge and data-driven modification adaptively. We show that our model
is able to outperform the other baseline methods for most of the dataset, and
show that using domain knowledge-guided data-learning iseffective.
