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相关概念视频

Molecular Models02:00

Molecular Models

43.9K
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.
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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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Nursing Code of Ethics01:29

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The Nursing Code of Ethics sets the ethical benchmark for the profession, and guides nurses in ethical analysis and decision making at the societal, organizational, and clinical levels. The code encompasses showing compassion and respect for the patient, their families, and communities in all circumstances while committing to providing patient-centered care. In addition, the code states that nurses must advocate for the patient by defending a cause or recommendation to protect their rights,...
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Group Design02:01

Group Design

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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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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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Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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WeMol:一个基于云的零代码平台,用于人工智能驱动的分子设计和模拟.

Haihan Liu1,2, Xin Yan2, Hao Fang1

  • 1Department of Medicinal Chemistry, State Key Laboratory of Discovery and Utilization of Functional Components in Traditional Chinese Medicine, Shandong Key Laboratory of Druggability Optimization and Evaluation for Lead Compounds, Shandong Basic Science Academic Special Zone/Research Center (Pharmacy), School of Pharmaceutical Sciences, Cheelo College of Medicine, Shandong University, Jinan, Shandong 250012, P.R. China.

Journal of chemical information and modeling
|February 11, 2026
PubMed
概括
此摘要是机器生成的。

WeMol是一个用户友好的,人工智能驱动的药物发现平台. 它集成了多个计算工具,使复杂的分子建模可供研究人员使用,而不需要编码技能,加速早期药物设计.

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科学领域:

  • 计算化学是一种计算化学.
  • 人工智能在药物发现中的作用
  • 分子建模分子建模

背景情况:

  • 现代药物发现面临着碎片化工具和高技术障碍的挑战.
  • 人工智能 (AI) 在药物发现中的广泛采用受到可用性问题的限制.
  • 需要集成的,用户友好的平台来简化早期药物设计.

研究的目的:

  • 为了介绍WeMol,一个人工智能驱动的,一站式分子计算平台.
  • 降低缺乏编程专业知识的研究人员在计算药物发现中的入门障碍.
  • 通过可访问和多功能解决方案,加速早期药物设计和发现.

主要方法:

  • 开发WeMol,一个集成的人工智能驱动的分子计算平台.
  • 集成模块用于分子相似性搜索,对接 (基于结构和AI增强),ADMET预测,分子生成和分子动力学.
  • 实现基于云的零代码接口,用于执行计算工作流.

主要成果:

  • WeMol提供了一个全面的工具套件,用于早期药物发现.
  • 该平台使得没有编码技能的研究人员能够执行复杂的分子计算.
  • WeMol简化了计算工作流程的构建和执行.

结论:

  • WeMol提供了一种多功能,可访问和可重复的解决方案,以加速药物发现.
  • 这种由人工智能驱动的平台有效地将先进的算法与实际的药物设计应用程序集成在一起.
  • 通过减少技术障碍,WeMol赋予了更广泛的研究人员在计算药物发现方面的权力.