一个双模态图形学习框架,用于识别化学和生物技术药物之间的相互作用事件
Zhongying Ru1,2, Yangyang Wu1, Jinning Shao3
1Center for Data Science, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China.
Briefings in bioinformatics
|July 28, 2023
概括
这项研究介绍了CB-TIP,这是一种用于预测涉及化学药物和生物技术药物的药物相互作用 (DDI) 的新框架. 它使用双模态图表数据库 (CB-DB) 来提高DDI预测的准确性.
科学领域:
- 药理学和药物发现
- 计算化学计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 药物相互作用 (DDI) 在医学上至关重要,但目前的预测方法往往不包括生物技术药物.
- 生物技术药物具有较高的特异性和较少的副作用,因此需要将其纳入DDI分析.
- 现有的模型主要侧重于小分子化学药物,在预测大分子生物技术药物的相互作用方面存在差距.
研究的目的:
- 为化学药物和生物技术药物开发一个全面的框架来预测事件意识的药物相互作用 (DDI).
- 构建一个大型的双模态图形数据库 (CB-DB),集成多种相互作用事件和分子结构.
- 解决目前DDI预测方法的局限性,这些方法不包括生物技术药物.
主要方法:
- 开发了CB-DB,这是一个双模态图形数据库,包含基于相互作用事件的化学/生物技术药物和内源蛋白.
- 定制的CB-TIP,一个基于图形的框架,利用图形表示学习进行DDI预测.
- 从分子结构和相互作用方式对药物表示的对比混合用于端到端的预测.
主要成果:
- CB-DB集成了异构的分子结构 (药物,蛋白质) 和相互作用事件.
- CB-TIP有效地从双重模式生成药物表示.
- 实验表明CB-TIP在预测药物相互作用方面具有显著优势.
结论:
- CB-TIP为DDI预测提供了一种强大的方法,包括化学药物和生物技术药物.
- 双模态图表数据库和框架对发现新型药物相互作用充满希望.
- 这项工作推进了DDI预测,包括大型分子生物技术药物,这对现代医学至关重要.
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