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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

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Interpretable, probability-based confidence metric for continuous quantitative structure-activity relationship

Christopher E Keefer¹, Gregory W Kauffman, Rishi Raj Gupta

¹Computational ADME Group, Department of Pharmacokinetics, Dynamics, and Drug Metabolism, Pfizer Inc., Groton, Connecticut 06340, USA. christopher.keefer@pfizer.com

Journal of Chemical Information and Modeling

|January 25, 2013

Summary

This summary is machine-generated.

This study introduces a new method for quantitative structure-activity relationship (QSAR) models. It combines chemical similarity and activity landscape data for more reliable prediction confidence and applicability domain (AD) assessment.

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Area of Science:

Computational chemistry
Cheminformatics
Predictive modeling

Background:

Quantitative structure-activity relationship (QSAR) models are crucial in drug discovery and chemical research.
Current applicability domain (AD) measures often focus solely on structural similarity, potentially overlooking other factors.
The local activity landscape of compounds is an underappreciated aspect influencing QSAR prediction accuracy.

Purpose of the Study:

To develop a novel approach for assessing prediction confidence and applicability domain (AD) in QSAR models.
To integrate chemical similarity with the local activity landscape for a more robust confidence measure.
To create an interpretable confidence metric applicable across diverse datasets.

Main Methods:

Defining nearest neighbors (NN) based on chemical similarity.
Incorporating the NN activity landscape with a similarity-weighted root-mean-square distance (wRMSD) calculation.
Calibrating the calculated value into an intuitive confidence metric for prospective use.

Main Results:

A new method is presented that pairs chemical similarity and activity landscape information.
This approach generates a single, calculated confidence value for QSAR predictions.
The method converts this value into an interpretable confidence metric.

Conclusions:

The proposed approach offers a more comprehensive assessment of QSAR prediction confidence and applicability domain.
Integrating activity landscape alongside chemical similarity enhances the reliability of predictions.
The developed metric provides an intuitive and informative measure for prospective QSAR model application.