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相关概念视频

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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Newman Projections02:06

Newman Projections

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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.
The organic molecules rotate across the single bonds leading to numerous temporary three-dimensional structures of varying energy known as...
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Molecular Shapes01:18

Molecular Shapes

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Molecules have characteristic shapes that are crucial for their function. The arrangement of various electron groups around the central atom dictates their molecular geometry. Electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between the electron pairs by maximizing the distance between them. The valence electrons form either bonding pairs, located primarily between bonded atoms, or lone pairs.
Two regions of electron density in a diatomic...
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Hückel's Rule Diagram of π MOs: Frost Circle01:08

Hückel's Rule Diagram of π MOs: Frost Circle

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The Frost circle or the inscribed polygon method is a graphical method for determining the relative energies of π molecular orbitals (MOs) for planar, fully conjugated, and monocyclic compounds. This method was first described by A. A. Frost and Boris Musulin in 1953.
A Frost circle is constructed by drawing a polygon whose number of edges is equal to the number of carbons of the given cyclic system, with one of the vertices pointing down. Then, a circle is drawn enclosing the polygon so that...
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Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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pV-Diagrams01:18

pV-Diagrams

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The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...
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相关实验视频

Updated: Jan 14, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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通过层次的Grad-CAM图表解释性来揭示分子部分.

Salvatore Contino1, Paolo Sortino2, Maria Rita Gulotta3

  • 1Department of Engineering, University of Palermo, Viale delle Scienze, Palermo, 90128, Sicily, Italy. salvatore.contino01@unipa.it.

BMC bioinformatics
|October 23, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的可解释图形神经网络 (GNN) 框架,用于识别药物发现的关键分子子结构. 该方法提高了虚拟查的准确性,并通过揭示绑定驱动器来帮助合理的药物设计.

关键词:
计算机辅助药物设计 (CADD)图形可解释性 图形可解释性图形神经网络的神经网络分子部分的分子部分.虚拟查 虚拟查

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相关实验视频

Last Updated: Jan 14, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Modeling an Enzyme Active Site using Molecular Visualization Freeware
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科学领域:

  • 计算化学计算化学
  • 药物发现 药物发现 药物发现
  • 机器学习 机器学习

背景情况:

  • 虚拟查 (VS) 对于有效识别潜在的候选药物至关重要.
  • 图形神经网络 (GNN) 擅长模拟分子结构,但缺乏可解释性.
  • 解释药物发现的GNN预测仍然是一个挑战,阻碍了合理的治疗设计.

研究的目的:

  • 为GNN在药物发现中开发一种可解释的AI方法.
  • 为了确定负责生物活动的特定分子亚结构.
  • 在虚拟选中增强GNN模型的可解释性.

主要方法:

  • 训练了20个GNN模型,用于对20个激酶标预测小分子活性.
  • 实现了层次化的Grad-CAM图形解释器 (HGE) 框架.
  • 在原子,环和整个分子层面利用Grad-CAM来分析结合稳定.

主要成果:

  • 在虚拟选中,GNN模型实现了最先进的性能.
  • HGE成功地确定了驱动蛋白质-配体结合的关键分子部分.
  • 对文献数据进行验证的解释器,正确注释药物向相互作用.

结论:

  • 开发的方法可以加速药物查和成功发现.
  • 了解基层结构的贡献有助于结构优化和药物重新利用.
  • 为治疗开发中的计算化学家提供了宝贵的见解.