Additional Data for Machine Learning–Enabled Virtual Screening of c-MET Inhibitors

Additional computational data supporting a publication on machine learning–enabled virtual screening. The data includes docking poses, key structures from clustered molecular dynamics simulation frames, and protein apo geometries. It was authored by John Trant and last updated on May 16, 2026.

Use Cases

• Validate docking protocols by analyzing the provided docking poses.
• Study protein-ligand interaction stability using key structures from molecular dynamics simulations.
• Benchmark computational methods using the provided protein apo geometries.
• Train machine learning models for binding affinity prediction based on the structural data.

Strengths

• Data is directly tied to a published scientific study on c-MET inhibitors.
• Includes multiple data types relevant to computational drug discovery: docking poses, MD simulation structures, and protein geometries.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and total data size are unknown, which may limit suitability assessment.

Provenance

Source

Borealis Harvested Dataverse

Collection Method

Generated via computational chemistry methods (docking, molecular dynamics simulations).

Freshness

Last updated 2026-05-16 04:10:20; freshness should be verified.