Supporting information for an in silico analysis of benzimidazole derivatives. The data likely contains results from structural stability and molecular docking studies against proteins from Staphylococcus aureus and Candida albicans. It was authored by Alejandro Morales-Bayuelo and hosted on Harvard Dataverse, with a last update recorded for May 2026.
Use Cases
- Train machine learning models for binding affinity prediction based on molecular docking scores.
- Analyze structure-activity relationships (SAR) based on the structural features of benzimidazole derivatives.
- Benchmark molecular docking protocols based on interactions with specific bacterial and fungal protein targets.
Strengths
- Data is associated with a specific, peer-reviewed research analysis described in the title.
- Hosted on the authoritative Harvard Dataverse platform, suggesting a degree of curation.
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
Provenance
- Source
- Harvard Dataverse
- Collection Method
- In silico computational analysis, likely involving molecular docking and simulation.
- Freshness
- Last updated 2026-05-29 19:29:05; freshness should be verified.