Top Ten Predicted Drugs for Autistic Disorder from HBGCN Model

A list of ten predicted drug candidates for autistic disorder generated by a Heterogeneous Biological Graph Convolutional Network (HBGCN). The model integrates multimodal biological information to predict drug-target interactions. The dataset was created by Haoran Zhu and last updated on 2026-05-19.

Use Cases

• Benchmarking drug-target interaction prediction models based on the described HBGCN methodology.
• Identifying candidate drugs for further investigation in autism spectrum disorder research.
• Studying the integration of multimodal biological data for drug discovery applications.

Strengths

• Dataset is small (5.5 KB), facilitating quick download and inspection.
• Results are derived from a model that integrates both direct and indirect biological meta-paths, as described.

Limitations

• The dataset contains only ten 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

Haoran Zhu via figshare

Collection Method

Generated by a Heterogeneous Biological Graph Convolutional Network (HBGCN) model.

Freshness

Last updated 2026-05-19 17:24:34; freshness should be verified.