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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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Drugs for Treatment of Crohn's Disease in IBD Using Immunomodulatory Agents01:29

Drugs for Treatment of Crohn's Disease in IBD Using Immunomodulatory Agents

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Crohn's disease is an inflammatory bowel disorder marked by chronic inflammation of the GI tract. Various treatment strategies for Crohn's disease are employed, such as immunomodulatory agents, glucocorticoids, and biologics or anti-TNF therapy. Azathioprine (Imuran), a commonly used immunomodulatory drug for Crohn's disease, is converted in the body to mercaptopurine, which inhibits purine biosynthesis and cell proliferation. Both are utilized in severe cases of Inflammatory Bowel...
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Inflammatory Bowel Disease IV: Pharmacological Management01:29

Inflammatory Bowel Disease IV: Pharmacological Management

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Upon diagnosis, managing Inflammatory Bowel Disease (IBD) involves addressing several crucial aspects. The primary goals include resting the bowel, correcting malnutrition, and providing symptomatic relief. Resting the bowel may consist of medications to reduce inflammation and promote healing. Correcting malnutrition is essential, often requiring dietary adjustments and nutritional supplements. Symptomatic relief aims to ease pain, diarrhea, and other discomforts in IBD.
Pharmacologic...
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Drugs for Treatment of Crohn's Disease in IBD Using Biologic Agents: Anti-TNF01:24

Drugs for Treatment of Crohn's Disease in IBD Using Biologic Agents: Anti-TNF

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Tumor Necrosis Factor (TNF), a proinflammatory cytokine, contributes significantly to the inflammation seen in Crohn's disease. It exists as soluble TNF and membrane-bound TNF, with actions mediated through TNF receptors (TNFR). TNFR activation leads to the release of proinflammatory cytokines, T-cell activation, collagen production, and leukocyte migration, all contributing to inflammation in Crohn's disease. Anti-TNF monoclonal antibodies, namely infliximab (Remicade), adalimumab...
127
Inflammatory Bowel Disease III: Diagnostic Studies and Management I-Nutritional Therapy01:30

Inflammatory Bowel Disease III: Diagnostic Studies and Management I-Nutritional Therapy

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Various diagnostic tests are employed in the diagnostic process for Inflammatory Bowel Disease (IBD), particularly to differentiate between Crohn's disease and ulcerative colitis.
Diagnostic studies
A colonoscopy is the definitive screening test, distinguishing ulcerative colitis from other colon diseases with similar symptoms. During a colonoscopy test, inflamed mucosa with exudate ulcerations can be observed, and biopsies are taken to determine the histologic characteristics of the...
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Investigating Target Gene Function in a CD40 Agonistic Antibody-induced Colitis Model using CRISPR/Cas9-based Technologies
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专注于NOD2:通过用户告知机器学习框架推进IBD药物发现.

Ruhi Choudhary1, Radhakrishnan Mahadevan1

  • 1Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada.

ACS medicinal chemistry letters
|July 17, 2024
PubMed
概括

优化可定制用户信息合成框架 (FOCUS) 是一种用于药物发现的新机器学习模型. 它使用专家知识和人工智能来产生新药候选药物,提高效率和类似药物的特性.

科学领域:

  • 人工智能在药物化学中的应用
  • 机器学习用于药物设计.
  • 计算机化药物发现技术

背景情况:

  • 药物发现是一个复杂而漫长的过程.
  • 生成式机器学习模型有可能加速候选人识别.
  • 将领域专业知识集成到AI模型中可以改善药物设计.

研究的目的:

  • 介绍优化可定制用户信息合成 (FOCUS) 模型的框架.
  • 展示FOCUS在生成优化的候选药物的能力.
  • 突出模型的可解释性和用户反集成.

主要方法:

  • 使用近距离政策优化 (PPO) 来指导蒙特卡罗树搜索 (MCTS).
  • 产生了潜在药物候选者的SMILES表示.
  • 集成的域名专业知识和用户反,用于代改进.
  • 采用SHAP和LIME用于模型的解释性.

主要成果:

  • 产生的候选药物具有优化的可药性和对NOD2,PEP和MCT1受体的结合效果.
  • 与Muramyl Dipeptide (MDP) 相比,已经证明了更好的相互作用概况.
  • 提供模型决策流程的透明分析.

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结论:

  • 焦点有效地将机器学习与专家洞察力融合在一起,以加速药物发现.
  • 该模型的可解释性增强了用户的信任,并允许专家驱动的调整.
  • 焦点代表了药物开发计算方法的重大进步.