MotifPiece: A Data-Driven Approach for Effective Motif Extraction and
  Molecular Representation Learning
Zhaoning Yu Hongyang Gao
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.