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相关概念视频

Drug Discovery: Overview01:26

Drug Discovery: Overview

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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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Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

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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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Protein Networks02:26

Protein Networks

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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,...
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Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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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...
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Principles of Drug Action01:24

Principles of Drug Action

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Drugs are chemical substances that modify biological responses by interacting with macromolecular targets such as receptors, ion channels, transporters, and enzymes. Pharmacodynamics describes the course of action of drugs leading to the physiological effect at a specific site in the body.
Drugs can be agonists or antagonists. Like the endogenous ligands, agonists always bind and activate the target to produce a cellular response. Agonist binding induces a conformational change which in turn...
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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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Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
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前进:逻辑网络干扰的学习框架,以优先考虑药物开发目标.

Saptarshi Sinha, Ella McLaren, Madhubanti Mullick

    bioRxiv : the preprint server for biology
    |July 29, 2024
    PubMed
    概括

    我们开发了F.O.R.W.A.R.D.,一种用于药物开发的新型AI框架,以改善目标优先级和预测临床试验成功. 这种方法在预测炎症性肠道疾病的试验结果方面实现了100%的准确性.

    科学领域:

    • 生物医学信息学 生物医学信息学
    • 计算生物学 计算生物学
    • 药物发现 药物发现 药物发现

    背景情况:

    • 基于目标的药物开发是昂贵和不精确的,人工智能提供了潜在的改进.
    • 炎症性肠病 (IBD) 是一个复杂的治疗挑战,由于多因素的起源.

    研究的目的:

    • 为了引入和验证F.O.R.W.A.R.D.的F.O.R.W.A.R.D. (基于结果的研究和药物开发框架),一个基于网络的目标优先级方法.
    • 为了评估F.O.R.W.A.R.D. 在预测炎症性肠道疾病的药物疗效方面的实用性.

    主要方法:

    • 在F.O.R.W.A.R.D.中 使用现实世界结果和在临床试验中的转录组数据上训练的机器学习分类器.
    • 它定义了缓解的分子特征,并整合了网络连接,以预测药物-向基因相互作用.
    • 该方法与210项涉及52个目标的临床试验进行了比较.

    主要成果:

    • 在F.O.R.W.A.R.D.中 通过各种目标,机制和试验设计,证明了100%的完美预测准确度.
    • 在体中进行的"0"阶段试验表明,试验设计有潜力,并重新评估失败的候选药物.

    结论:

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  • 在F.O.R.W.A.R.D.中 提供了一种强大,数据驱动的方法来增强药物发现和开发,提高精度和降低成本.
  • 该框架对其他治疗领域的适应性及其引导临床决策的潜力有望改变研发.