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.
Code (1)
Tasks
molecular representationRepresentation LearningSimilar Papers 제목 키워드 기반
Extraction of Protein Sequence Motif Information using PSO K-Means
The main objective of the paper is to find the motif information.The functionalities of the proteins are ideally found from their motif information which is extracted using various techniques like clustering with k-means…
ClusteringDirect Motif Extraction from High Resolution Crystalline STEM Images
During the last decade, automatic data analysis methods concerning different aspects of crystal analysis have been developed, e.g., unsupervised primitive unit cell extraction and automated crystal distortion and defects…
Vocal Bursts Intensity PredictionMotifExplainer: a Motif-based Graph Neural Network Explainer
We consider the explanation problem of Graph Neural Networks (GNNs). Most existing GNN explanation methods identify the most important edges or nodes but fail to consider substructures, which are more important for graph…
Graph Neural NetworkMotif-based Graph Self-Supervised Learning for Molecular Property Prediction
Predicting molecular properties with data-driven methods has drawn much attention in recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable success in various molecular generation and predi…
Molecular Property PredictionProperty PredictionRetrosynthesisSelf-Supervised LearningCometNet: Contextual Motif-guided Long-term Time Series Forecasting
Long-term Time Series Forecasting is crucial across numerous critical domains, yet its accuracy remains fundamentally constrained by the receptive field bottleneck in existing models. Mainstream Transformer- and Multi-la…
Time Series Forecasting