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Related Experiment Video

Updated: Jun 22, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Development and evaluation of a new statistical model for structure-based high-throughput virtual screening.

Shuxing Zhang¹, Lei Du-Cuny

¹Department of Experimental Therapeutics, The University of Texas M. D. Anderson Cancer Center, Unit 36, Houston, TX 77030, USA. shuzhang@mdanderson.org

International Journal of Bioinformatics Research and Applications

|June 16, 2009

Summary

We developed HiPCDock, a High-Performance Computing (HPC) tool for drug discovery. This efficient package uses bioinformatics for reliable, high-throughput virtual screening in drug development.

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Workflow and Tools for Crystallographic Fragment Screening at the Helmholtz-Zentrum Berlin

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Last Updated: Jun 22, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

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Workflow and Tools for Crystallographic Fragment Screening at the Helmholtz-Zentrum Berlin

Workflow and Tools for Crystallographic Fragment Screening at the Helmholtz-Zentrum Berlin

Published on: March 3, 2021

Area of Science:

Computational chemistry and bioinformatics
Drug discovery and development
Molecular modeling

Background:

High-throughput virtual screening (HTVS) is crucial for identifying drug candidates.
Existing molecular docking methods can lack statistical rigor, impacting screening reliability.
Bioinformatics approaches can enhance the statistical significance of computational screening results.

Purpose of the Study:

To develop an automated, user-friendly, and efficient molecular docking package for drug discovery.
To improve the statistical significance of virtual screening results using a bioinformatics approach.
To provide a robust tool for both computational experts and experimental scientists.

Main Methods:

Development of a High-Performance Computing (HPC)-based molecular docking scheme named HiPCDock.
Implementation of a bioinformatics approach, inspired by BLAST, to enhance statistical significance.
Validation of the statistical model using ten known Thymidine Kinase (TK) binders.

Main Results:

The HiPCDock scheme demonstrated improved statistical significance for known TK binders.
Real inhibitors exhibited statistically significant results, characterized by low probabilities and expectation values.
The developed package is automated, easy-to-use, and efficient for HTVS.

Conclusions:

HiPCDock offers an efficient and statistically robust platform for molecular docking-based virtual screening.
The integration of bioinformatics enhances the reliability of drug discovery screening processes.
The user-friendly design facilitates adoption by a wide range of scientific users in drug development.