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An R-Based Landscape Validation of a Competing Risk Model

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Published on: September 16, 2022

Managing bias in ROC curves.

Robert D Clark¹, Daniel J Webster-Clark

¹Tripos Informatics Research Center, 1699 South Hanley Road, Saint Louis, MO 63144, USA. bclark@tripos.com

Journal of Computer-Aided Molecular Design

|February 8, 2008

Summary

This study proposes two modifications to receiver operating characteristic (ROC) curve analysis for virtual screening. These enhancements improve the early identification of active compounds in drug discovery, optimizing screening efficiency.

Area of Science:

Computational chemistry
Cheminformatics
Drug discovery

Background:

Virtual screening methods are crucial for identifying potential drug candidates.
Standard receiver operating characteristic (ROC) curve analysis has limitations in evaluating screening efficiency.
Current methods may not adequately prioritize early identification of active compounds.

Purpose of the Study:

To propose novel modifications to ROC curve analysis for enhanced virtual screening evaluation.
To improve the ability of ROC curves to favor early identification of 'hits'.
To introduce methods for distinguishing biased from unbiased screening statistics.

Main Methods:

Introduction of semi-logarithmic plots (pROC plots) for ROC analysis and area under the curve (AUC) calculations.

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Development of two weighting schemes (arithmetic and harmonic) for active compounds based on lead series size.

Application of these modified methods to evaluate virtual screening performance.

Main Results:

Semi-logarithmic pROC plots effectively bias statistics towards early hit identification.
Weighted AUC calculations, particularly the harmonically weighted AUC, demonstrate improved ability to prioritize early identification of diverse active compounds.
The proposed weighting schemes successfully distinguish between biased and unbiased screening statistics.

Conclusions:

Modified ROC curve analysis using pROC plots and weighted AUC offers a more sensitive evaluation of virtual screening methods.
These enhancements are valuable for optimizing hit identification in early-stage drug discovery.
The harmonically weighted AUC is particularly effective for emphasizing early discovery of diverse active compound classes.