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Related Concept Videos

Review and Preview01:10

Review and Preview

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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
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Review and Preview01:13

Review and Preview

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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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Parallel Resonance01:23

Parallel Resonance

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The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
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Parallel Processing01:20

Parallel Processing

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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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Resistors In Parallel01:23

Resistors In Parallel

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Resistors are in parallel when one end of all the resistors are connected to a continuous wire of negligible resistance and the other end of all the resistors are also connected to one another through a continuous wire of negligible resistance. In the case of a parallel configuration, the potential drop across each resistor is the same. Current through each resistor can be found using Ohm’s law, I = V/R, where the voltage is constant across each resistor. The sum of the individual currents...
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Series and Parallel Capacitors01:14

Series and Parallel Capacitors

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Capacitors, fundamental components in electronic circuits, can be connected in series and/or parallel configurations. Each configuration has different impacts on the overall behavior of the circuit.
First, consider capacitors connected in series to a battery. In this configuration, the plate connected to the battery's positive terminal develops a positive charge, while the plate attached to the negative terminal becomes negatively charged. An equal magnitude of charge is induced on the...
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Use of Viral Entry Assays and Molecular Docking Analysis for the Identification of Antiviral Candidates against Coxsackievirus A16
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Parallelization of Molecular Docking: A Review.

Dong Dong1, Zhijian Xu2, Wu Zhong3

  • 1School of Computer Science, National University of Defense Technology, Changsha, China.

Current Topics in Medicinal Chemistry
|August 22, 2018
PubMed
Summary
This summary is machine-generated.

Accelerating molecular docking, a key virtual screening method, requires High-Performance Computing (HPC). This review details parallel techniques and algorithms for optimizing docking software on HPC platforms to handle big data challenges.

Keywords:
CADDDDGridHigh-performance computingMolecular dockingSARSVirtual screening.

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • Molecular docking is a crucial virtual screening technique for predicting ligand-target binding.
  • Computational complexity and big data necessitate High-Performance Computing (HPC) for efficient molecular docking.
  • Advances in parallel computing offer significant performance and accuracy improvements.

Purpose of the Study:

  • To review and detail the advances in accelerating molecular docking software using parallel High-Performance Computing (HPC) platforms.
  • To discuss novel parallel algorithms and optimizations for docking software.
  • To provide insights into techniques for parallelizing molecular docking.

Main Methods:

  • Review of existing literature on parallel molecular docking algorithms.
  • Analysis of software optimizations and porting to various HPC architectures.
  • Discussion of novel parallel algorithm designs and implementations.

Main Results:

  • Significant progress has been made in parallelizing molecular docking software for HPC environments.
  • Both optimization of existing programs and development of new parallel algorithms have been achieved.
  • Various HPC platforms have been leveraged to enhance docking performance and accuracy.

Conclusions:

  • Parallelization techniques are essential for advancing molecular docking in the era of big data.
  • HPC platforms provide the necessary computational power to overcome docking complexities.
  • Continued development in parallel algorithms will further enhance the efficiency and applicability of molecular docking in drug discovery.