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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

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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...
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Targets for Drug Action: Overview01:26

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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
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Drug Discovery: Overview01:26

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
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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...
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Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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LKE-DTA:通过大型语言模型表示和知识图嵌入来预测药物标结合亲和力.

Jielong Mou1, Yudong Yan1, Boren Jiang1

  • 1Chongqing Key Laboratory of Big Data for Bio Intelligence, School of Life Health Information Science and Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China.

Molecular diversity
|November 12, 2025
PubMed
概括

新的深度学习模型LKE-DTA通过整合大型语言模型 (LLM) 和知识图 (KG) 来增强药物发现,以准确地预测药物标结合亲和力 (DTA). 这种方法显著提高了预测准确度和概括能力.

关键词:
结合亲和力预测的预测.药物目标结合亲缘关系双重多头注意力注意力知识图表知识图表大型语言模型.

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科学领域:

  • 计算化学是一种计算化学.
  • 生物信息学是一种生物信息学.
  • 人工智能在药物发现中的作用

背景情况:

  • 准确的药物标结合亲和力 (DTA) 预测对于有效的药物发现至关重要.
  • 现有的计算方法在整合多样化的生物医学数据和建模复杂的分子相互作用方面面临着挑战.

研究的目的:

  • 开发一个新的深度学习框架,LKE-DTA,用于增强DTA预测.
  • 利用大型语言模型 (LLM) 和知识图 (KG) 来实现全面的分子表示.
  • 提高DTA预测模型的准确性和概括性.

主要方法:

  • 开发了LKE-DTA,这是一个集LLMs和KGs的深度学习框架.
  • 采用双重多头注意力机制,用于异质嵌入的动态融合.
  • 对基准数据集 (戴维斯,KIBA) 进行了全面评估,使用五重交叉验证和冷启动场景.

主要成果:

  • 在所有基准数据集中,LKE-DTA的表现始终优于最先进的方法.
  • 实现了平均平方误差 (MSE) 和平均绝对误差 (MAE) 的显著降低,并改善了对应指数 (CI) 和皮尔森相关系数 (r).
  • 在冷启动设置 (冷药物和冷目标) 和在独立测试组中表现出强烈的泛化.

结论:

  • 合并LLM和KG为DTA预测提供了一个强大的方法.
  • LKE-DTA为推进药物设计和精准医学提供了强大而准确的框架.
  • 这项工作突出了混合AI模型在应对复杂的生物医学挑战方面的潜力.