Correlation Analysis of Molecular Graph Indices and Drug Model Errors

Correlation results between Neighbourhood degree-based topological indices and predictive model error metrics for Gastroesophageal Reflux Disease (GERD) drug compounds. The dataset, authored by Mythili V and last updated on 2026-04-19, evaluates how structural descriptors relate to performance indicators like Mean Squared Error and Standard Error.

Use Cases

• Validate the predictive power of graph-based molecular descriptors based on correlation coefficients with model errors.
• Select relevant topological indices for QSAR modeling of GERD treatments based on their association with Mean Squared Error.
• Analyze the statistical relationship between molecular structure and pharmacological property prediction based on provided correlation and error metrics.

Strengths

• Focuses on a specific medical application (Gastroesophageal Reflux Disease drug compounds).
• Includes specific statistical metrics like Pearson correlation coefficient, Mean Squared Error, and Standard Error.
• Released under a permissive CC-BY-4.0 license.

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

Likely derived from computational analysis of molecular graphs and predictive models.

Freshness

Last updated 2026-04-19 13:39:00; freshness should be verified.