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Structure of Benzene: Molecular Orbital Model01:18

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According to the molecular orbital (MO) model, benzene has a planar structure with a regular hexagon of six sp2 hybridized carbons. As shown in Figure 1, each carbon is bonded to three other atoms with C–C–C and H–C–C bond angles of 120°. The C–H bond length is 109 pm, and the C–C bond length is 139 pm which is midway between the single bond length of sp3 hybridized carbons (154 pm) and sp2 hybridized carbons (133 pm).
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An acid can be deprotonated to form a conjugate base or an anion. If the produced anion is more stable, then the acid is stronger. On the contrary, if the anion is unstable, then the acid is weaker. Hence, to determine the acidity of the compound, the stability of its conjugate base is studied using various factors.
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Binary Acids and Bases
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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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Related Experiment Video

Updated: Jan 30, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

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Enspara: Modeling molecular ensembles with scalable data structures and parallel computing.

J R Porter1, M I Zimmerman1, G R Bowman1

  • 1Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, Missouri 63110, USA.

The Journal of Chemical Physics
|February 3, 2019
PubMed
Summary
This summary is machine-generated.

Markov state models (MSMs) provide insights into protein dynamics. The enspara library enhances MSM scalability for analyzing complex protein conformational changes.

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Area of Science:

  • Computational biology
  • Biophysics
  • Protein dynamics

Background:

  • Markov state models (MSMs) are essential for studying protein dynamics and conformational changes.
  • Current methods struggle with the vast conformational space of proteins, limiting scalability.
  • Identifying key features in large models is computationally challenging.

Purpose of the Study:

  • To present enspara, a novel library designed to enhance the scalability of Markov state models.
  • To introduce new algorithms and data structures for efficient MSM construction and analysis.
  • To enable the study of proteins with a greater number of degrees of freedom.

Main Methods:

  • Development of specialized data structures, including ragged arrays, to minimize memory usage.
  • Implementation of message passing interface (MPI)-parallelized algorithms for compute-intensive tasks.
  • Creation of a flexible framework for building and analyzing large-scale MSMs.

Main Results:

  • Dramatically improved scalability of traditional MSM methods.
  • Reduced memory requirements through the use of ragged arrays.
  • Enabled computationally tractable analysis of larger, more complex protein dynamics models.

Conclusions:

  • The enspara library significantly advances the computational feasibility of Markov state modeling.
  • It offers a scalable solution for analyzing protein conformational dynamics.
  • Enables deeper insights into protein mechanisms by handling larger feature sets.