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The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

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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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Overview
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The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
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Atomic Mass01:52

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Atoms — and the protons, neutrons, and electrons that compose them — are extremely small. For example, a carbon atom weighs less than 2 × 10−23 g. When describing the properties of tiny objects such as atoms, we use appropriately small units of measure, such as the atomic mass unit (amu). The amu was originally defined based on hydrogen, the lightest element, then later in terms of oxygen. Since 1961, it has been defined with regard to the most abundant isotope of carbon, atoms of which...
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Atomic Orbitals02:44

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An atomic orbital represents the three-dimensional regions in an atom where an electron has the highest probability to reside. The radial distribution function indicates the total probability of finding an electron within the thin shell at a distance r from the nucleus. The atomic orbitals have distinct shapes which are determined by l, the angular momentum quantum number. The orbitals are often drawn with a boundary surface, enclosing densest regions of the cloud.
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The Energies of Atomic Orbitals03:21

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In an atom, the negatively charged electrons are attracted to the positively charged nucleus. In a multielectron atom, electron-electron repulsions are also observed. The attractive and repulsive forces are dependent on the distance between the particles, as well as the sign and magnitude of the charges on the individual particles. When the charges on the particles are opposite, they attract each other. If both particles have the same charge, they repel each other.
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Related Experiment Video

Updated: Jan 26, 2026

Examining BCL-2 Family Function with Large Unilamellar Vesicles
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BCL::Mol2D-a robust atom environment descriptor for QSAR modeling and lead optimization.

Oanh Vu1, Jeffrey Mendenhall1, Doaa Altarawy2,3

  • 1Department of Chemistry, Center for Structural Biology, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, TN, 37235, USA.

Journal of Computer-Aided Molecular Design
|April 8, 2019
PubMed
Summary
This summary is machine-generated.

New BCL::Mol2D descriptors outperform Molprint2D in computer-assisted drug discovery. This novel, reversible descriptor enables better machine learning model predictions and aids in visualizing pharmacophores for lead optimization.

Keywords:
CheminformaticsMolecular descriptorPharmacophore mappingQSARSensitivity analysis

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Fragment-based molecular fingerprints are crucial in computer-assisted drug discovery.
  • Atom environment (AE) descriptors, like Molprint2D, are widely used for compound screening.
  • Molprint2D showed strong performance in identifying active compounds across various targets.

Purpose of the Study:

  • Introduce BCL::Mol2D descriptors as an advancement over Molprint2D.
  • Evaluate the performance of BCL::Mol2D in drug discovery tasks.
  • Demonstrate the utility of BCL::Mol2D's reversibility for model interpretability and lead optimization.

Main Methods:

  • Developed BCL::Mol2D descriptors based on a universal AE library.
  • Compared BCL::Mol2D with Molprint2D on nine diverse PubChem datasets.
  • Trained artificial neural networks with dropout using both descriptor sets.
  • Combined BCL::Mol2D with Reduced Short Range descriptors.
  • Visualized 'pharmacophore maps' using BCL::Mol2D's reversible property.

Main Results:

  • BCL::Mol2D descriptors outperformed Molprint2D on nine PubChem datasets.
  • Artificial neural networks trained on BCL::Mol2D showed up to 26% improvement in logAUC.
  • BCL::Mol2D demonstrated reversibility, allowing decomposition of predictions to substructures.
  • A modest improvement was observed when BCL::Mol2D was combined with Reduced Short Range descriptors.
  • Demonstrated visualization of pharmacophore maps for kinase inhibitors.

Conclusions:

  • BCL::Mol2D represents a significant improvement over Molprint2D for computer-assisted drug discovery.
  • The reversibility of BCL::Mol2D enhances machine learning model interpretability and guides lead optimization.
  • BCL::Mol2D is a valuable tool for identifying and optimizing drug candidates, particularly for targets like serine/threonine kinases.