  The study of molecule-target interaction is quite important for drug
discovery in terms of target identification, hit identification, pathway study,
drug-drug interaction, etc. Most existing methodologies utilize either
biomedical network information or molecule structural features to predict
potential interaction link. However, the biomedical network information based
methods usually suffer from cold start problem, while structure based methods
often give limited performance due to the structure/interaction assumption and
data quality. To address these issues, we propose a pseudo-siamese Graph Neural
Network method, namely MTINet+, which learns both biomedical network
topological and molecule structural/chemical information as representations to
predict potential interaction of given molecule and target pair. In MTINet+,
1-hop subgraphs of given molecule and target pair are extracted from known
interaction of biomedical network as topological information, meanwhile the
molecule structural and chemical attributes are processed as molecule
information. MTINet+ learns these two types of information as embedding
features for predicting the pair link. In the experiments of different
molecule-target interaction tasks, MTINet+ significantly outperforms over the
state-of-the-art baselines. In addition, in our designed network sparsity
experiments , MTINet+ shows strong robustness against different sparse
biomedical networks.
