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相关概念视频

Protein Networks02:26

Protein Networks

4.0K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.0K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.3K
VSEPR Theory for Determination of Electron Pair Geometries
34.3K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
12.5K
Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
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...
12.8K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.4K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.4K
Gene Families01:57

Gene Families

8.8K
Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...
8.8K

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相关实验视频

Updated: Jul 2, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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属性意识关系网络用于短暂的分子属性预测.

Quanming Yao, Zhenqian Shen, Yaqing Wang

    IEEE transactions on pattern analysis and machine intelligence
    |February 21, 2024
    PubMed
    概括

    我们为人工智能辅助的药物发现开发了PAR (Property-Aware Relation networks),通过有限的数据改进了分子性质预测. 我们的方法通过考虑属性特定的关系来增强分子嵌入,在短暂的学习场景中超越现有技术.

    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
    • 生物信息学是一种生物信息学.

    背景情况:

    • 分子性质预测对于人工智能辅助的药物发现至关重要,但由于有限的标记数据而面临挑战,这是少数射击学习问题的特征.
    • 现有的方法往往难以捕捉分子之间的动态,属性特定的关系.

    研究的目的:

    • 引入物质意识关系网络 (PAR),旨在应对少数射击分子性质预测挑战.
    • 开发一种可转移版本 (T-PAR),能够处理药物发现中常见的分销转换.

    主要方法:

    • PAR使用一种属性意识的分子编码器来生成属性特定的分子嵌入.
    • 一个查询依赖关系图的学习模块通过模拟与目标属性相关的分子间关系来改进这些嵌入.
    • T-PAR采用联合采样和关系图学习方案,用于跨源域和目标域的同时学习,以减轻分布转移.

    主要成果:

    • 与现有方法相比,PAR显著提高了对少量射击分子性质预测任务的性能.
    • T-PAR在可转移的少数射击分子性质预测方面表现出卓越的结果,有效地处理域位移.
    • 废除和案例研究证实了PAR和T-PAR的有效性和设计理由.

    结论:

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    A Protocol for Computer-Based Protein Structure and Function Prediction

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    • PAR和T-PAR为药物发现中的少量和可转移的少量分子性质预测提供了有效的解决方案.
    • 拟议的方法利用属性特定的关系和域适应技术来增强分子表示学习.
    • 这些进展有望加速在人工智能驱动的药物发现管道中识别候选分子.