Reliably assessing prediction reliability for high dimensional QSAR data.

Area of Science:

• Computational chemistry
• Cheminformatics
• Drug discovery

Background:

• Quantitative Structure-Activity Relationship (QSAR) models are crucial for drug discovery.
• Traditional validation methods and applicability domains often fail to guarantee QSAR model prediction reliability.
• High-dimensional data presents significant challenges to the predictive power of existing QSAR methods.

Purpose of the Study:

• To develop a novel predictive classifier, TreeEC, for assessing QSAR model prediction reliability.
• To evaluate the effectiveness of prediction confidence as a measure of reliability compared to applicability domains.
• To demonstrate TreeEC's robustness in handling high-dimensional QSAR data.

Main Methods:

• Development of the TreeEC predictive classifier.
• Comparison of prediction confidence and applicability domain approaches for reliability assessment.
• Evaluation of TreeEC performance on 11 public datasets against traditional methods.

Main Results:

• Prediction confidence is a more reliable indicator of QSAR model performance than applicability domains.
• External samples with >95% prediction confidence from TreeEC achieved 94% accuracy, versus 84% average accuracy.
• TreeEC demonstrates superior performance and reduced sensitivity to high dimensionality compared to other popular QSAR methods.

Conclusions:

• TreeEC offers a reliable method for assessing QSAR model prediction reliability, particularly for high-dimensional datasets.
• Prediction confidence is a superior metric for ensuring reliable QSAR predictions.
• TreeEC provides a robust and user-friendly tool for computational chemists and drug discovery researchers.