Determining the validity of a QSAR model--a classification approach.

Area of Science:

• Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) modeling
• Cheminformatics and computational chemistry
• Machine learning in drug discovery and materials science

Background:

• Assessing the predictive accuracy of QSAR models for novel chemical entities is crucial.
• Existing scoring techniques are often model-specific.
• A generalizable method is needed to evaluate QSAR model performance on new compounds.

Purpose of the Study:

• To develop and validate a novel technique for predicting the performance of existing QSAR models on new compounds.
• To establish a method applicable to linear regression models, with potential for generalization to other quantitative models.
• To assess the reliability of QSAR predictions for unseen chemical structures.

Main Methods:

• A classification-based approach was developed, categorizing regression residuals from a trained QSAR model into 'good' and 'bad' prediction classes.
• A classifier was trained on these categorized residuals.
• The trained classifier was then applied to new compounds to predict the class of their residuals, indicating prediction quality.

Main Results:

• The technique was tested on diverse datasets covering physical and biological properties, using QSAR models of varying quality.
• A range of linear and nonlinear classifiers were evaluated for their performance.
• Weighted success rates for predicting whether a new compound would be well or poorly predicted ranged from 73% to 94% for the optimal classifier.

Conclusions:

• The presented technique effectively determines the predictive performance of established QSAR models for new compounds.
• The method demonstrates robustness across different datasets and QSAR model qualities.
• This approach offers a valuable tool for enhancing the reliability and applicability of QSAR/QSPR models in scientific research.