Molecular Docking and Simulation Data for 1,3-Thiazine Derivatives as Antimicrobials

Computational modeling data for 1,3-thiazine derivatives as antimicrobial agents, including molecular docking, molecular dynamic simulation, and ADMET prediction results. The dataset was authored by Sana Khan and published on figshare under a CC-BY-4.0 license. It was last updated on May 26, 2026.

Use Cases

• Training predictive models for antimicrobial activity based on molecular docking scores.
• Validating molecular dynamic simulation protocols using provided trajectory data.
• Developing ADMET property prediction models for thiazine derivative compounds.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• File size is 6.8 MB, indicating a focused and manageable download.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Computational modeling via molecular docking, molecular dynamic simulation, and ADMET prediction.

Freshness

Last updated 2026-05-26 08:20:57; freshness should be verified.