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Parallel Processing

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

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Updated: Oct 6, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A Review on Parallel Virtual Screening Softwares for High-Performance Computers.

Natarajan Arul Murugan¹, Artur Podobas¹, Davide Gadioli²

¹Department of Computer Science, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-10044 Stockholm, Sweden.

Pharmaceuticals (Basel, Switzerland)

|January 21, 2022

Summary

This summary is machine-generated.

Computational drug discovery accelerates identifying potential drug candidates by using supercomputers. Parallel processing of in silico virtual screening methods significantly reduces the time and cost compared to experimental screening.

Keywords:

chemical space computational drug discovery high-performance computers and accelerators molecular docking parallelization virtual screening

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Area of Science:

Biopharmaceutical research
Computational chemistry
Drug discovery and development

Background:

Drug discovery is a complex, costly, and time-intensive process.
Identifying lead compounds with high binding affinity, specificity, and favorable ADMET properties is crucial.
Vast chemical spaces pose significant challenges for computational drug discovery.

Purpose of the Study:

To review parallelization algorithms for in silico virtual screening.
To discuss scoring functions and search algorithms in drug discovery.
To analyze the performance of docking software on high-performance computing architectures.

Main Methods:

In silico virtual screening using supercomputers.
Parallel implementation of screening algorithms.
Analysis of scoring functions and search algorithms.
Performance evaluation of docking software on HPC.

Main Results:

Parallelization of virtual screening enables efficient exploration of large chemical spaces.
Supercomputers with reliable scoring functions aid in identifying potential drug candidates.
High-performance computing (HPC) architectures can significantly speed up drug discovery processes.

Conclusions:

Parallel in silico virtual screening is a cost-effective and time-efficient approach for drug discovery.
Optimized algorithms and scoring functions are key to successful computational drug discovery.
HPC is essential for tackling the computational demands of modern drug discovery.