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Improving drug discovery through parallelism.

Jerónimo S García1, Savíns Puertas-Martín1,2, Juana L Redondo1

  • 1Supercomputing - Algorithms Research Group (SAL), Agrifood Campus of International Excellence, University of Almería, Carretera Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain.

The Journal of Supercomputing
|January 23, 2023
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Summary
This summary is machine-generated.

We developed pOptiPharm, a parallel algorithm for ligand-based virtual screening. This approach enhances prediction quality and significantly reduces computation time compared to sequential methods.

Keywords:
Evolutionary algorithmsHigh-performance computingShape similarityVirtual screening

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Ligand-based virtual screening (LBVS) is crucial for identifying potential drug candidates.
  • Current LBVS methods face limitations in prediction quality and computational time.
  • The OptiPharm algorithm previously improved sequential LBVS methods.

Purpose of the Study:

  • To parallelize the OptiPharm algorithm for enhanced performance in LBVS.
  • To reduce the time required for compound identification in drug discovery.
  • To improve the accuracy and efficiency of virtual screening predictions.

Main Methods:

  • Designed pOptiPharm, a novel two-layer parallelization strategy for the OptiPharm algorithm.
  • Automated molecule distribution across computational cluster nodes.
  • Parallelized core OptiPharm internal methods: initialization, reproduction, selection, and optimization.

Main Results:

  • pOptiPharm demonstrated improved prediction quality over sequential OptiPharm.
  • Computation time was reduced almost proportionally to the number of processing units used.
  • The parallelized approach effectively accelerates the drug discovery pipeline.

Conclusions:

  • pOptiPharm offers a significant advancement in accelerating and improving ligand-based virtual screening.
  • The parallelization strategy effectively addresses the time and quality limitations of traditional LBVS.
  • This software holds promise for more efficient and accurate identification of novel drug compounds.