Regression Outputs for GERD Drug QSPR/QSAR Models

Statistical regression outputs support reproducibility for quantitative structure-activity relationship modeling of Gastroesophageal Reflux Disease drug compounds. The dataset includes model coefficients, performance metrics like R² and RMSE, and significance tests. Author Mythili V published it on figshare in April 2026.

Use Cases

• Compare model performance using R², Adjusted R², and F-statistic values across different descriptor sets.
• Analyze the influence of specific molecular descriptors by examining their Regression Coefficients and associated p-values.
• Validate new predictive models by benchmarking their RMSE and MAE against the provided Residual Metrics.
• Select optimal feature sets for GERD compound modeling by reviewing the Descriptors Used column alongside model Standard Error.

Strengths

• Includes multiple validation metrics for model assessment, such as R², Adjusted R², RMSE, and MAE.
• Provides detailed statistical outputs like Regression Coefficients, p-values, and Standard Error for reproducibility.

Limitations

• The dataset size, including the number of models (rows) and compounds analyzed, is unspecified.
• The underlying molecular descriptor data and raw compound structures are not included, limiting direct re-analysis.

Provenance

Source

figshare, authored by Mythili V.

Collection Method

Generated from statistical regression analysis of QSAR/QSPR models for GERD drug compounds.

Freshness

Last updated in April 2026.