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Related Concept Videos

Molecular Models02:00

Molecular Models

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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.
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Different notations are used to represent the three-dimensional structure of molecules on two-dimensional surfaces. One of the most commonly used representations is the dash-wedge formula. The dashed wedges, solid wedges, and the plane lines indicate the groups situated behind the plane, coming out of the plane, and in the plane, respectively.
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Fischer Projections02:18

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Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines.
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Protein Diffusion in the Membrane01:24

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Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Related Experiment Video

Updated: Jun 25, 2025

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
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Equivariant score-based generative diffusion framework for 3D molecules.

Hao Zhang1, Yang Liu2, Xiaoyan Liu1

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.

BMC Bioinformatics
|May 30, 2024
PubMed
Summary

This study introduces EMDS, a novel deep learning framework for 3D molecular generation. EMDS outperforms existing methods in creating realistic molecules, accelerating drug discovery and protein design.

Keywords:
3D molecular generationE(3)-equivariantPartial score functionsScore-based diffusion model

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Area of Science:

  • Computational chemistry
  • Artificial intelligence in drug discovery
  • Molecular modeling

Background:

  • Manual molecular design is costly and time-consuming in drug discovery.
  • Deep learning offers an efficient approach to navigate chemical space.
  • Existing diffusion models have limitations for 3D molecular generation.

Purpose of the Study:

  • To introduce a novel E(3)-equivariant score-based diffusion framework (EMDS) for 3D molecular generation.
  • To address limitations of unified Gaussian diffusion methods.
  • To improve the efficiency and accuracy of molecular design.

Main Methods:

  • Developed a novel E(3)-equivariant score-based diffusion framework (EMDS).
  • Decomposed diffusion processes for distinct molecular feature components.
  • Integrated angle and torsion angle information into network models.

Main Results:

  • EMDS significantly outperformed state-of-the-art methods on the QM9 dataset for 3D molecular generation.
  • Ablation studies confirmed the effectiveness of key framework components.
  • Generated molecules closely resemble real-world chemical structures.

Conclusions:

  • EMDS demonstrates superior capacity in modeling chemically realistic molecules compared to previous methods.
  • The framework offers promising opportunities for complex biomedical and protein design challenges.
  • This advancement can accelerate drug discovery and protein engineering.