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Updated: Jul 6, 2026

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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Hydra: a self regenerating high performance computing grid for drug discovery.

Drew Bullard1, Alberto Gobbi, Matthew A Lardy

  • 1Anadys Pharmaceuticals, San Diego, California 92121, USA. dbullard@anadyspharma.com

Journal of Chemical Information and Modeling
|March 15, 2008
PubMed
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An affordable computational grid was built using existing and outdated computers to accelerate computer-aided drug design. This approach enhances drug discovery by leveraging readily available resources for molecular simulations and virtual screening.

Area of Science:

  • Computational chemistry
  • Pharmacology
  • Computer science

Background:

  • Computer-aided drug design (CADD) is crucial for modern drug discovery, evaluating vast numbers of compounds.
  • Molecular simulations incorporating protein and ligand flexibility increase computational demands.
  • Existing computational grids can be costly to implement and maintain.

Purpose of the Study:

  • To describe a low-cost implementation of a 165-node computational grid at Anadys Pharmaceuticals.
  • To utilize excess and outdated computing resources for drug discovery tasks.
  • To create a scalable and cost-effective computational infrastructure.

Main Methods:

  • A computational grid was established using 165 nodes, integrating excess desktop capacity and older machines.

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  • The Linux operating system was standardized across all nodes for uniformity.
  • coLinux virtualization software enabled running Linux within MS Windows on desktop computers.
  • HYDRA software was employed for computational modeling, virtual screening, and lead optimization.

    • Main Results:

    • The implemented grid effectively utilizes available computing power for CADD.
    • The system's performance scales with company size and technological advancements.
    • A cost-effective solution for high-performance computing in drug discovery was achieved.

    Conclusions:

    • Low-cost computational grids can significantly support and advance drug discovery efforts.
    • Repurposing existing and outdated hardware offers a viable alternative to expensive infrastructure.
    • This approach enhances the efficiency and scalability of computational drug design pipelines.