UHRF1 SRA Domain: Predicted Small Molecule Binders from 2.4M Library Screen

Approximately 2.4 million small molecules were screened virtually against the UHRF1 protein's SRA domain. The dataset compiles the predicted top hits from this structure-based screening, authored by Debasis Patnaik. Results are provided in formats for MS PowerPoint, MS Excel, and Schrodinger's Maestro software.

Use Cases

• Prioritizing experimental validation candidates based on predicted binding poses.
• Training machine learning models for binding affinity prediction using virtual screening results.
• Analyzing chemical space and scaffold preferences for molecules targeting the UHRF1 SRA domain.

Strengths

• Screens a large chemical library of approximately 2.4 million small molecules.
• Provides results in multiple formats, including software-specific Maestro Pose Viewer files.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the top hits is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Debasis Patnaik via paperswithcode.

Collection Method

Structure-based virtual screening of chemical libraries.