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相关概念视频

Molecular Models02:00

Molecular Models

37.9K
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.
37.9K
Molecular Orbital Theory II03:51

Molecular Orbital Theory II

19.0K
Molecular Orbital Energy Diagrams
19.0K
MO Theory and Covalent Bonding02:40

MO Theory and Covalent Bonding

10.3K
The molecular orbital theory describes the distribution of electrons in molecules in a manner similar to the distribution of electrons in atomic orbitals. The region of space in which a valence electron in a molecule is likely to be found is called a molecular orbital. Mathematically, the linear combination of atomic orbitals (LCAO) generates molecular orbitals. Combinations of in-phase atomic orbital wave functions result in regions with a high probability of electron density, while...
10.3K
Molecular Geometry and Dipole Moments02:36

Molecular Geometry and Dipole Moments

12.6K
The VSEPR theory can be used to determine the electron pair geometries and molecular structures as follows:
12.6K
Structure of Benzene: Kekulé Model01:07

Structure of Benzene: Kekulé Model

8.6K
In 1865, August Kekule suggested the structure of benzene according to the structural theory of organic chemistry based on the three assertions—formula of benzene is C6H6, all the hydrogens of benzene are equivalent, and each carbon must have four bonds due to its tetravalency.
He proposed that benzene has a cyclic structure of six carbon atoms attached to one hydrogen atom each, with three alternating pi bonds.
8.6K
Structure of Benzene: Molecular Orbital Model01:18

Structure of Benzene: Molecular Orbital Model

8.9K
According to the molecular orbital (MO) model, benzene has a planar structure with a regular hexagon of six sp2 hybridized carbons. As shown in Figure 1, each carbon is bonded to three other atoms with C–C–C and H–C–C bond angles of 120°. The C–H bond length is 109 pm, and the C–C bond length is 139 pm which is midway between the single bond length of sp3 hybridized carbons (154 pm) and sp2 hybridized carbons (133 pm).
8.9K

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相关实验视频

Updated: Jun 7, 2025

Modeling an Enzyme Active Site using Molecular Visualization Freeware
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Modeling an Enzyme Active Site using Molecular Visualization Freeware

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MolE:使用分离注意力的分子图的基础模型.

Oscar Méndez-Lucio1, Christos A Nicolaou2,3, Berton Earnshaw4

  • 1Recursion, Salt Lake City, UT, USA. oscar.mendez-lucio@recursion.com.

Nature communications
|November 12, 2024
PubMed
概括

MolE,一种用于分子图的新型变压器模型,改善了化学性质预测. 它在大型数据集上的两步预训练策略增强了药物发现任务的概括性.

科学领域:

  • 计算化学是一种计算化学.
  • 机器学习在药物发现中的作用
  • 化学信息学 化学信息学

背景情况:

  • 从分子结构准确预测化学性质在化学科学中至关重要.
  • 小规模的训练数据集限制了预测模型的概括能力.
  • 自主监督的大型未标记数据集的预训练,然后对标记数据进行微调已经成为解决方案.

研究的目的:

  • 介绍MoleE,一个适用于分子图的变压器架构.
  • 为增强分子性质预测开发一个两步预训策略.
  • 为了提高模型在预测ADMET属性的泛化性能.

主要方法:

  • 开发了MoleE,这是一个针对分子图表表示的变压器架构.
  • 实施了两步预训练策略: 1) 在842万个分子图表上进行自我监督学习,以了解化学结构,以及 2) 在生物信息上进行多任务学习.
  • 在较小的,标记为特定任务的数据集上微调预训练的MoleE模型.

主要成果:

  • 与现有的最先进的技术相比,微调的MoleE模型实现了更高的性能.
  • 在治疗数据共享排行榜中的22个ADMET任务中,在10个方面表现优于最好的公布结果.
  • 由于预训练策略,表现出更好的概括能力.

更多相关视频

Methods to Test Visual Attention Online
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Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
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Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

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相关实验视频

Last Updated: Jun 7, 2025

Modeling an Enzyme Active Site using Molecular Visualization Freeware
14:37

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

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Methods to Test Visual Attention Online
09:44

Methods to Test Visual Attention Online

Published on: February 19, 2015

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Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
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Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Published on: May 27, 2020

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结论:

  • MolE架构及其预训练策略显著提升了分子性质预测.
  • 这种方法为加速药物发现和开发提供了一个强大的工具.
  • 这些发现突出了针对化学信息学应用的大规模预培训的潜力.