深度药物:一种基于图形的一般深度学习框架,用于药物相互作用和药物向相互作用的预测
Qijin Yin1, Rui Fan2, Xusheng Cao2
1Ministry of Education Key Laboratory of Bioinformatics Research Department of Bioinformatics at the Beijing National Research Center for Information Science and Technology Center for Synthetic and Systems Biology Department of Automation Tsinghua University Beijing 100084 China.
DeepDrug是一种新的深度学习框架,可以准确预测药物相互作用 (DDI) 和药物向相互作用 (DTI). 这种计算工具可以加速药物发现,并帮助识别COVID-19等疾病的潜在治疗方法.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 准确预测药物相互作用 (DDI) 和药物向相互作用 (DTI) 对药物发现至关重要.
- 现有的计算方法由于不充分评估化学结构属性而存在局限性.
研究的目的:
- 开发一个统一的深度学习框架,DeepDrug,用于预测DDI和DTI.
- 通过学习药物和蛋白质的全面表示来克服以前方法的局限性.
主要方法:
- 使用剩余图卷积网络 (Res-GCNs) 来表示药物.
- 采用卷积网络 (CNN) 来表示蛋白质序列.
- 将这些集成到深度学习框架 (DeepDrug) 中,以实现统一的预测.
主要成果:
- 在各种DDI和DTI预测任务中,DeepDrug在最先进的方法中表现优越.
- 视觉化证实DeepDrug可以学习有意义的化学和结构特征.
- 应用DeepDrug用于药物重新定位,识别针对SARS-CoV-2的潜在候选者.
结论:
- DeepDrug 是一个有效的计算工具,用于预测DDI和DTI.
- 该框架提供了对生化相互作用机制的见解.
- 深度药物促进了药物重新定位和发现新型治疗剂.
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