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相关概念视频

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Cholinergic antagonists—such as antimuscarinics—are available in oral, topical, ocular, parenteral, and inhalational formulations. Most antimuscarinics are oral formulations,  while scopolamine is available as a topical patch, and ipratropium and tiotropium are available as inhalation aerosols or powders. Atropine, tropicamide, and cyclopentolate are topically instilled in the eye. Most antimuscarinics are lipid-soluble and readily absorbed from the gastrointestinal tract and...
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Intermittent intravenous (IV) infusion is a method of drug administration where medications are delivered over short infusion periods followed by intervals of no drug delivery. This approach helps to prevent sustained high drug concentrations in the bloodstream, reducing the risk of adverse effects associated with prolonged exposure. Unlike continuous infusion, steady-state concentrations may not be achieved during a single dosing cycle but can be reached through repeated...
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通过交替多任务学习策略,同时对8个体外试验进行小分子抗氧化剂的高度准确和可解释的预测.

Duancheng Zhao1, Yanhong Zhang1, Yihao Chen1

  • 1Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.

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概括

这项研究引入了一种新的深度学习模型,用于在八个测试中预测小分子抗氧化活性. 开发的FG-BERT模型提供了可靠的预测,并确定了关键的结构性贡献者,帮助新的抗氧化剂发现.

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 机器学习在化学中的应用

背景情况:

  • 小分子抗氧化剂在各种行业中至关重要,防止氧化损伤.
  • 传统的实验发现是耗时的;计算方法是未经探索的.

研究的目的:

  • 开发一种多任务自主监督学习方法,用于预测抗氧化剂活性.
  • 在八个常见的体外抗氧化剂试验中同时评估小分子.

主要方法:

  • 提出了一个基于功能组的交替多任务自我监督分子表示学习 (FG-BERT) 模型.
  • 该模型经过训练并对其预测抗氧化活性的能力进行了评估.

主要成果:

  • 在预测性能方面,FG-BERT模型显著超过了基线模型.
  • 获得了高平均F1 (0.860),BA (0.880),ROC-AUC (0.954) 和PRC-AUC (0.937) 的得分.
  • 证明了可靠的预测能力和出色的解释能力,识别了关键的结构碎片.

结论:

  • 开发的FG-BERT模型为抗氧化剂发现提供了强大的计算工具.
  • 创建了一个在线平台 (AOP),以方便访问这些预测功能.
  • 这种方法预计将加速新型小分子抗氧化剂的识别.