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The Quantum-Mechanical Model of an Atom02:45

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Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Acyclic diene metathesis polymerization or ADMET polymerization involves cross-metathesis of terminal dienes, such as 1,8-nonadiene, to give linear unsaturated polymer and ethylene. As ADMET is a reversible process, the formed ethylene gas must be removed from the reaction mixture to complete the polymerization process.
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In the macroscopic world, objects that are large enough to be seen by the naked eye follow the rules of classical physics. A billiard ball moving on a table will behave like a particle; it will continue traveling in a straight line unless it collides with another ball, or it is acted on by some other force, such as friction. The ball has a well-defined position and velocity or well-defined momentum, p = mv, which is defined by mass m and velocity v at any given moment. This is the typical...
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The ideal gas law is an approximation that works well at high temperatures and low pressures. The van der Waals equation of state (named after the Dutch physicist Johannes van der Waals, 1837−1923) improves it by considering two factors.
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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QM Calculations in ADMET Prediction.

Alfonso Pozzan1

  • 1Research Informatics, Computational Chemistry, Aptuit SRL, an Evotec Company, Verona, Italy. Alfonso.Pozzan@aptuit.com.

Methods in Molecular Biology (Clifton, N.J.)
|February 5, 2020
PubMed
Summary

Quantum mechanics (QM) methods enhance drug discovery by predicting ADMET properties. QM/MM and machine learning integration with QM descriptors improve drug metabolism and interaction studies.

Area of Science:

  • Computational chemistry
  • Pharmacology
  • Drug discovery

Background:

  • Quantum mechanics (QM) methods are increasingly used to predict the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profiles of small molecules.
  • These methods provide crucial molecular descriptors and physicochemical properties essential for accurate ADMET prediction.

Purpose of the Study:

  • To highlight the utility of QM methods in understanding drug properties and interactions.
  • To demonstrate how QM/MM and machine learning can advance drug discovery.

Main Methods:

  • Application of quantum mechanics (QM) for calculating molecular descriptors and physicochemical properties.
  • Utilizing mixed QM and molecular mechanics (QM/MM) for mechanistic studies of drug-cytochrome interactions.
Keywords:
ADMET predictionIn silico ADMETMetabolism predictionQuantum mechanics

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  • Integrating QM-derived descriptors with experimental data and machine learning algorithms.

    • Main Results:

    • QM methods enable the calculation of key properties influencing ADMET profiles.
    • QM/MM enhances the mechanistic understanding of drug metabolism and interactions with enzymes like cytochromes.
    • Combined approaches have led to user-friendly software impacting the drug discovery pipeline.

    Conclusions:

    • Quantum mechanics methods are vital for predicting small molecule ADMET properties.
    • Advanced computational techniques, including QM/MM and machine learning, significantly improve drug discovery efficiency.
    • The integration of computational and experimental data drives innovation in pharmaceutical research.