Edge Partitioning and Degree Data from GERD Drug Molecular Graphs

Edge partitioning results and node degree information derived from the graph structures of GERD drug compounds. The dataset was created by Mythili V and published on figshare in April 2026. It supports analysis of network topology and connectivity patterns.

Use Cases

• Analyze edge distribution patterns to understand connectivity in molecular graphs of GERD drugs.
• Study node degree metrics to identify central atoms or functional groups within the compound structures.
• Use edge partitioning data to validate or develop graph neural network models for molecular property prediction.
• Correlate degree-based topological features with chemical properties or biological activity of the compounds.

Strengths

• Data is derived from molecular graphs, providing a structured representation of chemical compounds.
• Explicitly designed for network topology and connectivity pattern analysis.
• Published under a permissive CC-BY-4.0 license for reuse.

Limitations

• Specific dataset size, row count, and column details are unknown.
• The description lacks concrete metrics like the number of graphs, nodes, or edges.
• No sample data or file format information is provided for preliminary inspection.

Provenance

Source

figshare, authored by Mythili V.

Collection Method

Derived from graph structures of GERD drug compounds; specific generation method unknown.

Freshness

Last updated in April 2026, indicating recent publication.