MULGA是一种基于自编码器的统一多视图图形方法,用于识别药物蛋白相互作用和药物重新定位
Jiani Ma1, Chen Li2, Yiwen Zhang3
1School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
我们开发了MULGA,这是一种用于预测药物蛋白相互作用 (DPI) 和实现药物重新定位的新框架. 通过有效利用多视图学习和图形自动编码器,MULGA提高了预测准确度,并识别了潜在的COVID-19治疗方法.
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.
- 药物发现 药物发现
背景情况:
- 识别药物蛋白相互作用 (DPI) 对于药物重新定位至关重要,加速新药开发.
- 现有的计算方法在相似性计算,异质信息整合和负样本选择方面面临挑战.
研究的目的:
- 为准确的药物蛋白相互作用 (DPI) 预测和药物重新定位提出一个统一的框架,MULGA.
- 为了解决当前DPI预测方法的局限性.
主要方法:
- 开发了一个多视图自动编码器 (MULGA) 框架.
- 用于药物和目标亲和力矩阵的多视图学习.
- 使用图形自编码器推断丢失的DPI.
- 实施了一种新的"因关联而有罪"负面抽样策略.
主要成果:
- 在DPI预测方面,MULGA显著超过了最先进的方法.
- 废弃性研究证实了单个MULGA成分的有效性.
- 确定了针对SARS-CoV-2尖端糖蛋白的有前途的候选药物,提供了潜在的COVID-19治疗见解.
结论:
- MULGA为DPI预测和药物重新定位提供了有效和统一的方法.
- 该框架为药物发现和重新利用提供了有价值的见解,特别是对于新出现的传染病.
- 作为一个开源工具,MULGA可用于学术研究.
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