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Future in biomolecular computation.

E Wimmer1

  • 1Cray Research Inc., Mendota Heights, MN 55120.

Journal of Computer-Aided Molecular Design
|January 1, 1988
PubMed
Summary
This summary is machine-generated.

Advancements in computational power will enhance biomolecular simulations, benefiting force-field methods most. New quantum mechanical approaches and hardware parallelism are key for tackling complex problems like protein folding.

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Area of Science:

  • Computational chemistry
  • Biophysics
  • Molecular modeling

Background:

  • Biomolecular computations utilize quantum mechanics and molecular dynamics.
  • Current methods include rigorous QM, semi-empirical QM, and force-field molecular dynamics.
  • These methods scale differently with system size.

Purpose of the Study:

  • To discuss the impact of increasing computational power on biomolecular simulations.
  • To explore future directions in computational methodologies.
  • To address challenges in biomolecular modeling, such as protein folding.

Main Methods:

  • Review of existing computational approaches (QM, semi-empirical QM, molecular dynamics).
  • Anticipation of hardware advancements (increased speed, parallelism).

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  • Discussion of methodological advancements (e.g., density functional theory).

    • Main Results:

    • Force-field methods will see the greatest benefit from increased computational power due to scaling.
    • Quantum mechanical methods will be applicable to larger systems.
    • Parallelism will become a standard feature in computer architectures.

    Conclusions:

    • A hierarchy of computational approaches is necessary for biomolecular systems.
    • Protein folding remains a significant challenge requiring potential new methodologies.
    • Future computational power will enable more complex biomolecular simulations.