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.

本文提出了一种数据驱动的基于统计度量的 MotifPiece 方法用于有效地提取分子中的结构模式，并通过引入异构学习模块作出了全面评估，表明其在提取结构模式方面具有改进，同时还展示了通过合并多个共享足够结构模式的数据集来进一步提高性能的方式。