mlsanityData to 7 Oct 2026

Graph Representation Learning for Interactive Biomolecule Systems

arXiv 2304.02656 ↗First posted 5 Apr 2023q-bio.QM cs.LGcited by 1

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Abstract (arXiv)

Advances in deep learning models have revolutionized the study of biomolecule systems and their mechanisms. Graph representation learning, in particular, is important for accurately capturing the geometric information of biomolecules at different levels. This paper presents a comprehensive review of the methodologies used to represent biological molecules and systems as computer-recognizable objects, such as sequences, graphs, and surfaces. Moreover, it examines how geometric deep learning models, with an emphasis on graph-based techniques, can analyze biomolecule data to enable drug discovery, protein characterization, and biological system analysis. The study concludes with an overview of the current state of the field, highlighting the challenges that exist and the potential future research directions.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 59 of its 247 references that are in the map. First posted on arXiv 5 Apr 2023.

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59 of 247 references.

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  1. 2025Pruning and Quantization Impact on Graph Neural Networkscited by 0