Top Ten Predicted Drugs for Depressive Disorder from HBGCN Model

A list of ten drug candidates predicted for depressive disorder by a Heterogeneous Biological Graph Convolutional Network (HBGCN). The dataset was published by author Haoran Zhu on figshare in May 2026. It is available as a 5.5 KB Excel file under a CC-BY-4.0 license.

Use Cases

• Ranking drug candidates for depressive disorder based on predicted drug-target interactions.
• Benchmarking new drug-target prediction models against the HBGCN results.
• Investigating the molecular mechanisms of drug regulation via associated proteins and gene expression patterns mentioned in the description.

Strengths

• Dataset is small (5.5 KB), making it easy to download and inspect.
• Source code and full dataset for the HBGCN model are publicly available on GitHub, allowing for reproducibility.

Limitations

• The dataset contains only 10 predicted drugs, representing a very limited scope.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Generated by the HBGCN model as described in the associated research.

Collection Method

Computational prediction via a Heterogeneous Biological Graph Convolutional Network.

Freshness

Last updated 2026-05-19 17:24:33; freshness should be verified.