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BRUSELAS: HPC Generic and Customizable Software Architecture for 3D Ligand-Based Virtual Screening of Large Molecular

Antonio J Banegas-Luna1, José P Cerón-Carrasco1, Savíns Puertas-Martín2

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BRUSELAS is a new web server for fast 3D ligand screening. It combines multiple algorithms for unbiased drug discovery, showing promise in identifying potential antidiabetic drugs.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • High-throughput screening requires efficient computational tools.
  • Existing ligand screening servers may have limitations in speed or accuracy.
  • 3D shape and pharmacophore searches are crucial for identifying drug candidates.

Purpose of the Study:

  • To introduce BRUSELAS (balanced rapid and unrestricted server for extensive ligand-aimed screening), a novel web software architecture.
  • To provide a highly efficient platform for 3D shape and pharmacophore searches.
  • To evaluate BRUSELAS's reliability in drug discovery compared to existing servers.

Main Methods:

  • BRUSELAS integrates a diverse set of shape and pharmacophore similarity algorithms.
  • Consensus scoring functions are employed to ensure unbiased results.
  • The server's performance was benchmarked against other established screening tools like USR-VS, SwissSimilarity, and ChemMapper.
  • The search for potential antidiabetic drugs was used as a case study for evaluation.

Main Results:

  • BRUSELAS demonstrated high efficiency in performing 3D shape and pharmacophore searches.
  • The consensus scoring approach yielded reliable and unbiased search outcomes.
  • Comparative analysis indicated competitive or superior performance of BRUSELAS against other servers.
  • The tool successfully facilitated the identification of potential antidiabetic drug candidates.

Conclusions:

  • BRUSELAS offers a novel, efficient, and reliable solution for large-scale ligand screening.
  • The web server's architecture and consensus scoring enhance drug discovery efforts.
  • BRUSELAS is a valuable, freely accessible resource for researchers in computational chemistry and drug discovery.