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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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PED:一种用于阿尔茨海默症药物分子生成的新型预测器-编码器-解码器模型.

Dayan Liu1, Tao Song1, Kang Na2

  • 1College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China.

Frontiers in artificial intelligence
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概括

我们开发了一种新型的人工智能模型,PED,以有效设计抑制乙胆酶 (AChE) 的分子,用于阿尔茨海默病 (AD) 治疗. 这种模型可以产生没有副作用的向化合物,加速药物发现并增强AI的认知能力.

关键词:
这就是阿尔茨海默病的原因.深度学习是一种深度学习.药物设计 药物设计分子生成分子的产生.神经网络的神经网络的神经网络

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科学领域:

  • 人工智能在药物发现中的作用
  • 神经科学和药理学 神经科学和药理学
  • 计算化学计算化学

背景情况:

  • 阿尔茨海默病 (AD) 是一种进展性神经退行性疾病,治疗选择有限.
  • 乙胆酶 (AChE) 抑制剂用于AD,但通常会引起副作用.
  • 目前用于分子设计的AI模型可能是低效的,需要额外的优化步骤.

研究的目的:

  • 介绍PED,一种使用变化自编码器 (VAE) 的认知条件分子设计模型.
  • 创建一个分子图书馆,以识别潜在的ACHE抑制剂,以最小的不良影响来治疗AD.
  • 开发一个高效的人工智能系统,同时进行属性预测和分子生成.

主要方法:

  • 在绑定DB中的ACHE活性化合物上微调预训练的VAE模型 (PED).
  • 利用条件分子生成为针对性基于性质的分子设计.
  • 使用分子对接来验证产生的分子与ACHE的结合亲和力.

主要成果:

  • PED有效地产生具有所需性质的分子,优于基准方法.
  • 该模型展示了化学空间表示的有效学习.
  • 生成的分子对AChE表现出强烈的结合亲和力,表明潜在的治疗疗效.

结论:

  • PED模型为设计用于阿尔茨海默病的新型ACHE抑制剂提供了一种高效和有效的方法.
  • 这种人工智能驱动的系统加速了AD疗法的发现,并改善了安全性.
  • 该研究强调了先进AI在提高药物发现和认知能力方面的潜力.