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

Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

48.5K
Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...
48.5K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

510
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...
510
Hydrogen Bonds01:04

Hydrogen Bonds

7.9K
A hydrogen bond is formed when a weakly positive hydrogen atom already bonded to one electronegative atom (for example, the oxygen in the water molecule) is attracted to another electronegative atom from another polar molecule, such as water (H2O), hydrogen fluoride (HF), or ammonia (NH3). The huge electronegativity difference between the H atom (2.1) and the atom to which it is bonded (4.0 for an F atom, 3.5 for an O atom, or 3.0 for an N atom), combined with the very small size of an H atom...
7.9K
Drug-Receptor Bonds01:25

Drug-Receptor Bonds

2.7K
Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
In...
2.7K
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
Ligand Binding Sites02:40

Ligand Binding Sites

12.7K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
12.7K

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

A Transformer for Reaction-Aware Compound Explorations with GFlowNet in QSAR-Guided Molecular Design.

Journal of chemical information and modeling·2026
Same author

Molecular optimization using a conditional transformer for reaction-aware compound exploration with reinforcement learning.

Communications chemistry·2025
Same author

Development of tolerance to bedaquiline by overexpression of trypanosomal acetate: succinate CoA transferase in Mycobacterium smegmatis.

Communications biology·2025
Same author

PRA-MutPred: Predicting the Effect of Point Mutations in Protein-RNA Complexes Using Structural Features.

Journal of chemical information and modeling·2025
Same author

Mothra: Multiobjective <i>de novo</i> Molecular Generation Using Monte Carlo Tree Search.

Journal of chemical information and modeling·2024
Same author

IEV2Mol: Molecular Generative Model Considering Protein-Ligand Interaction Energy Vectors.

Journal of chemical information and modeling·2024

相关实验视频

Updated: Jun 4, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.7K

DiffInt:一种基于结构的药物设计的扩散模型,具有明确的键相互作用指导.

Masami Sako1, Nobuaki Yasuo2, Masakazu Sekijima1

  • 1Department of Computer Science, Institute of Science Tokyo, Yokohama, Kanagawa 226-8501, Japan.

Journal of chemical information and modeling
|December 19, 2024
PubMed
概括
此摘要是机器生成的。

DiffInt是一种新的基于结构的药物设计方法,可以准确地模拟蛋白质-药物键. 与现有的深度学习模型相比,这种方法显著改善了结合能量的预测.

更多相关视频

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

371
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

17.0K

相关实验视频

Last Updated: Jun 4, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.7K
Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

371
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

17.0K

科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 医学中的人工智能

背景情况:

  • 基于结构的药物设计 (SBDD) 对于有效的药物发现至关重要.
  • 深度生成模型具有先进的3D分子生成.
  • 现有的模型很难准确地捕捉蛋白质-连接体相互作用,特别是键.

研究的目的:

  • 介绍DiffInt,一种新的SBDD方法.
  • 为了明确地建模关键相互作用,专注于键.
  • 为了提高药物向相互作用预测的准确性.

主要方法:

  • DiffInt将键视为伪粒子,用于自然的结合.
  • 该模型利用深度生成方法来进行3D分子生成.
  • 蛋白质 - 连接体相互作用的明确建模是该方法的核心.

主要成果:

  • DiffInt成功地复制了蛋白质和连接体之间的键.
  • 该模型在预测结能量的方面表现出卓越的性能.
  • 实验结果显示,与现有模型相比,实验结果显著改善.

结论:

  • DiffInt为基于结构的药物设计提供了一个强大的新工具.
  • 键的明确建模提高了预测的准确性.
  • DiffInt的开源可用性促进了更广泛的研究和应用.