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Related Concept Videos

Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Determination of Crystal Structures01:29

Determination of Crystal Structures

In the late 1800s, the revelation that light extended beyond visible wavelengths led to the discovery of X-rays by Wilhelm Roentgen. Recognized as high-energy electromagnetic radiation with short wavelengths, X-rays prompted exploration into their interaction with crystals. Max von Laue proposed in 1912 that the periodic arrangement of atoms, ions, or molecules in crystals would cause them to diffract X-rays, a hypothesis confirmed through experiments with copper sulfate and zinc sulfide...
Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...
Crystal Field Theory - Tetrahedral and Square Planar Complexes02:46

Crystal Field Theory - Tetrahedral and Square Planar Complexes

Tetrahedral Complexes
Crystal field theory (CFT) is applicable to molecules in geometries other than octahedral. In octahedral complexes, the lobes of the dx2−y2 and dz2 orbitals point directly at the ligands. For tetrahedral complexes, the d orbitals remain in place, but with only four ligands located between the axes. None of the orbitals points directly at the tetrahedral ligands. However, the dx2−y2 and dz2 orbitals (along the Cartesian axes) overlap with the ligands less than the dxy,...
Crystal Density01:19

Crystal Density

The crystal lattice structure of a material allows us to determine how many molecules exist in its unit cell. With this information, alongside the unit-cell parameters - three distance parameters (a, b, c) and three angular parameters (α, β, γ).Density (ρ) = (Z × M) / (a × b × c × NA)where:Z is the number of formula units per unit cellM is the molar mass of the substancea, b, and c are the edge lengths of the unit cellNA is Avogadro’s numberFor a simple cubic lattice, atoms are located only at...
Ionic Crystal Structures02:42

Ionic Crystal Structures

Ionic crystals consist of two or more different kinds of ions that usually have different sizes. The packing of these ions into a crystal structure is more complex than the packing of metal atoms that are the same size.
Most monatomic ions behave as charged spheres, and their attraction for ions of opposite charge is the same in every direction. Consequently, stable structures for ionic compounds result (1) when ions of one charge are surrounded by as many ions as possible of the opposite...

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Related Experiment Video

Updated: Jun 13, 2026

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
08:35

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Published on: May 29, 2021

Chemical space sampling by different scoring functions and crystal structures.

Natasja Brooijmans1, Christine Humblet

  • 1Structural Biology and Computational Chemistry, Wyeth Research, 401 N Middletown Road, Pearl River, NY, USA. reprints@prodigy.net

Journal of Computer-Aided Molecular Design
|April 20, 2010
PubMed
Summary
This summary is machine-generated.

Virtual screening for drug discovery benefits from using multiple protein structures and scoring functions. Combining these approaches explores diverse chemical spaces, identifying a broader range of potential drug leads.

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Last Updated: Jun 13, 2026

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Structural biology

Background:

  • Virtual screening (VS) is crucial for identifying novel drug leads.
  • Optimizing VS requires understanding the interplay between scoring functions and protein structures.
  • Best practices for utilizing diverse crystal structures and scoring methods in VS are not well-established.

Purpose of the Study:

  • To evaluate the impact of different crystal structures and scoring functions on virtual screening outcomes for PI3K-gamma.
  • To investigate consensus scoring strategies for retrospective VS.
  • To assess the influence of ligand preparation protocols on VS enrichment.

Main Methods:

  • Employed prospective and retrospective VS experiments using multiple PI3K-gamma crystal structures.
  • Utilized Glide SP and Prime MM-GBSA scoring functions, including consensus scoring.
  • Examined the effects of various ligand preparation protocols.

Main Results:

  • Different crystal structures prioritize distinct chemical spaces and chemotypes.
  • Scoring functions also exhibit chemotype-specific prioritization.
  • Prime MM-GBSA generally yielded lower enrichment factors compared to Glide SP.
  • Ligand preparation protocols impact VS enrichment.

Conclusions:

  • Multiple crystal structures and scoring functions are complementary in VS.
  • Combining these methods expands the chemical diversity explored for experimental follow-up.
  • This integrated approach enhances the identification of novel drug candidates.