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Related Experiment Video

Updated: Jun 27, 2026

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
08:35

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Published on: May 29, 2021

Data management system for distributed virtual screening.

Ting Zhou1, Amedeo Caflisch

  • 1Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.

Journal of Chemical Information and Modeling
|December 17, 2008
PubMed
Summary
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We developed a distributed virtual screening data management system (DVSDMS) for high throughput docking (HTD). DVSDMS efficiently manages large datasets and job distribution, enabling rapid analysis of millions of molecules.

Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • High throughput docking (HTD) is crucial for drug discovery but presents significant data management challenges.
  • Existing systems often struggle with the scale and efficiency required for processing millions of molecular interactions.

Purpose of the Study:

  • To develop an efficient and scalable distributed virtual screening data management system (DVSDMS).
  • To facilitate effective data handling and job distribution for high throughput docking campaigns.

Main Methods:

  • Developed DVSDMS utilizing MySQL for data management and job distribution.
  • Implemented a separation of data management from docking and ranking applications.
  • Tested DVSDMS on a kinase inhibitor identification campaign using a Linux PC cluster.

Related Experiment Videos

Last Updated: Jun 27, 2026

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
08:35

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Published on: May 29, 2021

Main Results:

  • DVSDMS efficiently manages and distributes jobs for docking millions of molecules.
  • The system allows real-time monitoring and prompt analysis of docking results.
  • Demonstrated efficient processing of up to 10^8 poses and a distribution rate of ~60 molecules/second in stress tests.

Conclusions:

  • DVSDMS provides an effective solution for managing large-scale HTD data.
  • The system is scalable and can efficiently support large compute clusters (up to 40,000 cores).
  • DVSDMS enhances the efficiency of virtual screening in drug discovery pipelines.