  The prediction modeling of drug-target interactions is crucial to drug
discovery and design, which has seen rapid advancements owing to deep learning
technologies. Recently developed methods, such as those based on graph neural
networks (GNNs) and Transformers, demonstrate exceptional performance across
various datasets by effectively extracting structural information. However, the
benchmarking of these novel methods often varies significantly in terms of
hyperparameter settings and datasets, which limits algorithmic progress. In
view of these, we conduct a comprehensive survey and benchmark for drug-target
interaction modeling from a structure perspective, via integrating tens of
explicit (i.e., GNN-based) and implicit (i.e., Transformer-based) structure
learning algorithms. To this end, we first unify the hyperparameter setting
within each class of structure learning methods. Moreover, we conduct a
macroscopical comparison between these two classes of encoding strategies as
well as the different featurization techniques that inform molecules' chemical
and physical properties. We then carry out the microscopical comparison between
all the integrated models across the six datasets, via comprehensively
benchmarking their effectiveness and efficiency. Remarkably, the summarized
insights from the benchmark studies lead to the design of model combos. We
demonstrate that our combos can achieve new state-of-the-art performance on
various datasets associated with cost-effective memory and computation. Our
code is available at
\hyperlink{https://github.com/justinwjl/GTB-DTI/tree/main}{https://github.com/justinwjl/GTB-DTI/tree/main}.
