Negative Data for Virtual High-Throughput Screening Pipeline Validation

An alternative method for validating drug discovery pipelines generates practically unlimited negative data without additional experimental cost. The approach randomizes ligands across published structures and creates structural isomers of known binders to produce matched positive and negative sets. The 5.8 MB document by Stefan M. Ivanov, last updated in March 2026, is shared under a CC-BY-4.0 license on figshare.

Use Cases

• Benchmarking molecular docking tools based on generated negative ligand-structure pairs.
• Validating multi-step virtual screening workflows based on the described matched positive and negative data sets.
• Assessing cheminformatics tool performance based on molecular property-matched negative data.
• Training machine learning models for binding prediction based on the described method for generating decoy compounds.

Strengths

• Dataset generation method requires no additional experimental work or expenditure.
• The approach can generate practically unlimited amounts of negative data.
• Generated negative data points are described as closely matching positive data in key molecular properties.

Limitations

• The primary file is a 5.8 MB DOCX document; data structure and format require manual inspection.
• Row count and specific data schema are unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

figshare

Collection Method

Computational generation via ligand randomization and structural isomer creation from published experimental structures.

Freshness

Last updated 2026-03-18 07:37:37