  Motif extraction is an important task in motif based molecular representation
learning. Previously, machine learning approaches employing either rule-based
or string-based techniques to extract motifs. Rule-based approaches may extract
motifs that aren't frequent or prevalent within the molecular data, which can
lead to an incomplete understanding of essential structural patterns in
molecules. String-based methods often lose the topological information inherent
in molecules. This can be a significant drawback because topology plays a vital
role in defining the spatial arrangement and connectivity of atoms within a
molecule, which can be critical for understanding its properties and behavior.
In this paper, we develop a data-driven motif extraction technique known as
MotifPiece, which employs statistical measures to define motifs. To
comprehensively evaluate the effectiveness of MotifPiece, we introduce a
heterogeneous learning module. Our model shows an improvement compared to
previously reported models. Additionally, we demonstrate that its performance
can be further enhanced in two ways: first, by incorporating more data to aid
in generating a richer motif vocabulary, and second, by merging multiple
datasets that share enough motifs, allowing for cross-dataset learning.
