Aromatase Inhibitor 3D-QSAR Computational Chemistry Data

Computational chemistry data from a 2018 study on human aromatase inhibitors. The dataset supports 3D-QSAR modeling of steroidal and azaheterocyclic compounds, as published in the Journal of Cheminformatics. It is provided by the U.S. Environmental Protection Agency.

Use Cases

• Perform 3D-QSAR modeling using descriptors like Dual Descriptor to predict inhibitor activity.
• Analyze protein-ligand interaction profiles, including Hydrophobic Contact and Nitrogen Heme Iron Coordination.
• Compare molecular features of Steroid and Azaheterocycle compound classes for inhibitor design.

Strengths

• Data is directly associated with a peer-reviewed publication in the Journal of Cheminformatics.
• Includes specific chemical descriptors relevant for 3D-QSAR, such as Dual Descriptor and Hydrophobic Contact.
• Provided by the authoritative U.S. Environmental Protection Agency.

Limitations

• The raw description states the data is very complex and requires expert knowledge in computational chemistry to use.
• Key structural details like row count, column names, and sample data are unavailable.
• The dataset was last updated in November 2020, which may limit its relevance for recent methodological advances.

Provenance

Source

U.S. Environmental Protection Agency

Collection Method

Computational chemistry data generated for a 3D-QSAR study.

Time Range

Associated with a 2018 publication.

Freshness

Last updated on 2020-11-12.