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High Content Screening in Neurodegenerative Diseases

High Content Screening in Neurodegenerative Diseases

Published on: January 6, 2012

Challenging Reverse Screening: A Benchmark Study for Comprehensive Evaluation.

Mingna Li¹, Jianxing Hu¹, Yanxing Wang¹

¹State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Xueyuan Road 38, Haidian District, 100191, Beijing, P.R. China.

Molecular Informatics

|November 17, 2021

Summary

This summary is machine-generated.

Reverse docking efficiently predicts drug targets and binding modes. A new dataset evaluated four docking programs, revealing Glide (SP and XP) best identifies true targets, even with ranking biases.

Keywords:

Benchmark Dataset Evaluation Reverse docking Target prediction

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Last Updated: Jun 21, 2026

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Area of Science:

Computational chemistry
Drug discovery
Bioinformatics

Background:

Reverse docking is crucial for computational target prediction and binding mode identification.
Existing docking tools lack comprehensive evaluation, hindering understanding of their strengths and limitations in target fishing.

Purpose of the Study:

To develop a novel evaluation dataset for reverse docking.
To assess the performance of prominent docking programs using this dataset.
To analyze the impact of inter-target ranking bias on prediction accuracy.

Main Methods:

Creation of a tailored evaluation dataset with true positive (core set) and negative (similar and dissimilar decoy sets) examples.
Performance evaluation of four docking programs: AutoDock, AutoDock Vina, Glide (SP), and GOLD.
Analysis of prediction performance under varying degrees of inter-target ranking bias.

Main Results:

A biased prediction performance was observed concerning binding site properties across the evaluated programs.
Glide (SP) and Glide (XP) demonstrated superior ability in identifying true targets.
This superior performance was consistent regardless of the presence or degree of inter-target ranking bias.

Conclusions:

The developed dataset effectively assesses reverse docking tool performance.
Glide (SP and XP) are recommended for reliable computational target fishing due to their robustness against ranking biases.
Further studies are needed to address binding site property biases in docking predictions.