Fingerprint-based clustering applied to define a QSAR model use radius.

Area of Science:

• Computational Chemistry
• Cheminformatics
• Drug Discovery

Background:

• Quantitative Structure-Activity Relationship (QSAR) models are crucial for evaluating compound properties like skin permeability and druglikeness.
• Existing QSAR validation methods often lack clear boundaries for model applicability.
• A defined limit is needed to understand the scope within which QSAR models can reliably predict molecular behavior.

Purpose of the Study:

• To develop a novel QSAR model validation method that defines the operational boundaries of predictive models.
• To introduce the concept of a 'QSAR model use radius' based on similarity to training data.
• To enable the selection of QSAR models with proven predictivity beyond their original training sets.

Main Methods:

• A new validation approach was created, moving beyond simple correlation to quantify expected correlation based on training set similarity.
• The method involves iterative clustering and systematic increases in dissimilarity to define training and test sets.
• Training sets are constructed by selecting one compound per cluster at increasing dissimilarity levels, with the model predicting remaining compounds.

Main Results:

• The QSAR model use radius effectively identifies the similarity threshold for reliable predictions.
• This approach provides a more rigorous validation than traditional methods, especially for large, redundant chemical datasets.
• The technique was successfully illustrated using models for skin permeability, drug/safe compound classification, and kinase inhibitor/drug classification.

Conclusions:

• The QSAR model use radius offers a robust metric for assessing QSAR model applicability domain.
• This method enhances the reliability of QSAR predictions by clearly defining when a model's predictions can be trusted.
• It is particularly valuable for large-scale chemical data analysis where traditional cross-validation is less effective.