Training, validation, and testing data support the research paper "3D Structural Analysis of Plasmodium Falciparum to Detect Inhibitors." The dataset, authored by Arham Wasti and hosted on Harvard Dataverse, is intended for applying machine learning to malaria drug discovery. It was last updated on April 25, 2026.
Use Cases
- Train machine learning models for inhibitor detection based on 3D structural features mentioned in the description
- Validate computational drug discovery pipelines using the provided validation split
- Benchmark predictive models for anti-malarial compound activity using the testing data
Strengths
- Data is explicitly structured for machine learning with dedicated training, validation, and testing splits
- Associated with a specific, peer-reviewed research paper providing context
- Hosted on the authoritative Harvard Dataverse platform
Limitations
- Column-level documentation is absent; field semantics must be inferred after download
- Row count is unknown, which may limit suitability assessment
- Description metadata is limited; actual data quality requires manual inspection after download
Provenance
- Source
- Harvard Dataverse
- Collection Method
- Likely generated from computational 3D structural analysis for the associated research paper.
- Freshness
- Last updated 2026-04-25 09:00:34; freshness should be verified