MotifPiece: A Data-Driven Approach for Effective Motif Extraction and Molecular Representation Learning

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.