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相关概念视频

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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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
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在异质图上进行深度多实例学习,用于药物-疾病关联预测.

Yaowen Gu1, Si Zheng2, Bowen Zhang3

  • 1Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS&PUMC), Beijing, 100020, China; Department of Chemistry, New York University, NY, 10027, USA.

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概括

通过使用深度多重实例学习来预测药物疾病关联 (DDAs),MilGNet增强了药物重新定位. 这种新的方法通过从异质网络中的路径实例中学习来提高准确性和可解释性.

关键词:
药物重新定位 药物重新定位药物疾病关联预测预测.不同质的图形神经网络的神经网络.的元路径.多个实例的学习是多个实例的学习.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 药理学 药理学是指药理学的学科.
  • 人工智能的人工智能是人工智能.

背景情况:

  • 药物重新定位通过确定现有药物的新用途来加速药物发现.
  • 目前用于预测药物疾病关联 (DDAs) 的方法往往缺乏对路径实例级学习的端到端框架.
  • 在路径实例中利用拓信息可以导致更精确和可解释的DDA预测.

研究的目的:

  • 引入MilGNet,这是一个用于药物重新定位的新型深度多个实例学习框架.
  • 开发一种端到端的方法,从药物疾病异质网络中的路径实例中学习.
  • 为了提高DDA预测的准确性和可解释性.

主要方法:

  • MilGNet使用异质图神经网络 (HGNN) 编码器用于药物和疾病节点嵌入.
  • 一个伪元路径生成器从药物疾病对 (袋) 创建多个元路径实例.
  • 一个双向实例编码器和基于注意力的多尺度预测器精制和汇总实例表示以进行预测.

主要成果:

  • 在五个基准数据集中,MilGNet显著超过了十种先进方法.
  • 该方法在袋子和实例级别实现了准确和可解释的预测.
  • 案例研究表明,MilGNet有潜力识别新的治疗适应症.

结论:

  • MilGNet提供了一种强大而可解释的药物重新定位方法.
  • 该框架有效地利用拓信息来增强DDA预测.
  • MilGNet有可能加速新药疗法的发现.