  With the increasing attention to molecular machine learning, various
innovations have been made in designing better models or proposing more
comprehensive benchmarks. However, less is studied on the data preprocessing
schedule for molecular graphs, where a different view of the molecular graph
could potentially boost the model's performance. Inspired by the Byte-Pair
Encoding (BPE) algorithm, a subword tokenization method popularly adopted in
Natural Language Processing, we propose GraphBPE, which tokenizes a molecular
graph into different substructures and acts as a preprocessing schedule
independent of the model architectures. Our experiments on 3 graph-level
classification and 3 graph-level regression datasets show that data
preprocessing could boost the performance of models for molecular graphs, and
GraphBPE is effective for small classification datasets and it performs on par
with other tokenization methods across different model architectures.
