Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Molecular Models02:00

Molecular Models

40.4K
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.
40.4K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

36.0K
VSEPR Theory for Determination of Electron Pair Geometries
36.0K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
101
Induced-fit Model01:13

Induced-fit Model

82.2K
Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical...
82.2K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.1K
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
1.1K
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.9K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

The 2026 global roadmap for textile-integrated wearable technologies in health.

Physiological measurement·2026
Same author

Computational Drug Repurposing Predicts FDA-Approved Drugs as Potential Inhibitors of Chikungunya Virus nsP2 Protease.

The journal of physical chemistry. B·2026
Same author

Bio-Inspired Microstructured Poly(vinylidene fluoride-co-hexafluoropropylene) Films Incorporated with Silver Nanoparticles for Antibacterial Applications.

Polymers·2026
Same author

Biowaste-Derived Catalysts for Sustainable Electrochemical Water Splitting: A Pathway to Circular Bioeconomy.

ChemSusChem·2026
Same author

Dynamics of Aβ42 Tetramer by REST2-CHARMM36m Simulations.

The journal of physical chemistry. B·2026
Same author

In silico investigation of the role of local and global inflammation-driven feedback in myelopoiesis and clonal cell expansion.

Journal of theoretical biology·2026

相关实验视频

Updated: Sep 12, 2025

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.7K

基于生成的人工智能模型的优化向分子设计增强的优化.

Tarek Khater1, Sara Awni Alkhatib1,2, Aamna AlShehhi1

  • 1Department of Biomedical Engineering and Biotechnology, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates.

Journal of cheminformatics
|August 4, 2025
PubMed
概括
此摘要是机器生成的。

生成型人工智能 (GenAI) 模型通过设计新型分子来加速药物发现. 本综述详细介绍了改善GenAI准确性,有效性和类似药物的特性以提高分子设计的技术.

关键词:
化学信息学 化学信息学药物发现 药物发现生成性AI是一种人工智能.分子设计分子设计.优化优化 优化优化强化学习是一种强化学习.

更多相关视频

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.2K
Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function
05:57

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function

Published on: April 26, 2024

488

相关实验视频

Last Updated: Sep 12, 2025

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.7K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.2K
Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function
05:57

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function

Published on: April 26, 2024

488

科学领域:

  • 药用化学 医学化学
  • 计算化学的计算化学
  • 人工智能的人工智能

背景情况:

  • 生成型人工智能 (GenAI) 提供了强大的能力,用于设计药物发现中的新分子.
  • 目前的GenAI应用在预测准确性,分子有效性和优化药物样性质方面面临着挑战.

研究的目的:

  • 为提高GenAI在分子设计中的性能提供技术的全面分析.
  • 探索GenAI驱动药物发现的进展和解决局限性的问题.

主要方法:

  • 关键生成架构的审查:变异自编码器,生成对抗网络和基于变压器的模型.
  • 讨论进展:强化学习,多目标优化和化学知识的整合.
  • 考察挑战:数据质量,模型可解释性和客观函数.

主要成果:

  • 基因人工智能架构为多样化的分子设计做出了独特的贡献.
  • 先进的技术提高了分子有效性,新性和药物相似性.
  • 持续存在的挑战需要进一步的研究,以获得最佳的GenAI应用.

结论:

  • 基因人工智能是药物发现的变革性工具,使复杂分子的设计成为可能.
  • 提供了战略指导,以克服局限性并增强GenAI在分子设计中的作用.
  • 本综述是研究人员利用GenAI在药物开发中的关键资源.