Related Experiment Video
Updated: Mar 15, 2026
08:49
Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
Published on: June 20, 2025
1.5K
DockBench as docking selector tool: the lesson learned from D3R Grand Challenge 2015
Veronica Salmaso1, Mattia Sturlese1, Alberto Cuzzolin1
1Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, Padua, Italy.
Journal of Computer-Aided Molecular Design
|September 18, 2016
Summary
Structure-based drug design (SBDD) uses molecular docking to optimize drug leads. DockBench platform testing in D3R Grand Challenge 2015 showed promising results for predicting accurate ligand-target binding poses.
Area of Science:
- Computational chemistry
- Drug discovery
- Structural biology
Background:
- Structure-based drug design (SBDD) is crucial for optimizing drug candidates.
- Accurate three-dimensional target-ligand complex structures, often from X-ray crystallography, are key for SBDD.
- Molecular docking models are used when complex structures are unavailable, but face challenges in predicting binding modes and energies.
Purpose of the Study:
- To evaluate the performance of different molecular docking and scoring methods.
- To assess the utility of the DockBench informatics platform for comparing docking protocols.
- To determine the accuracy of blind docking predictions for drug design.
Main Methods:
- Utilized the D3R Grand Challenge 2015 dataset for blind prediction assessments.
- Employed the DockBench platform to automate the comparison of various docking/scoring methods.
- Evaluated docking performance based on Root Mean Square Deviation (RMSD) metrics.
Main Results:
- DockBench facilitated the automated comparison of multiple molecular docking protocols.
- Blind prediction results demonstrated encouraging performance, particularly for pose prediction.
- Several protein-ligand complexes were predicted with accuracy suitable for medicinal chemistry applications.
Conclusions:
- The DockBench platform is a valuable tool for assessing and selecting optimal molecular docking strategies.
- Accurate pose prediction through molecular docking is achievable and beneficial for drug discovery.
- Further development and validation of docking protocols are essential for advancing SBDD.
