深度RT:基于分子性质的深度神经网络,预测途径模块中的化合物存在,并将其分类为模块类.
Hayat Ali Shah1, Juan Liu1, Zhihui Yang1
1Institute of Artificial Intelligence, School of Computer Science, Wuhan University, P. R. China.
Journal of bioinformatics and computational biology
|August 26, 2023
概括
一个新的深度学习模型,DeepRT,准确地预测复合路径模块关系. 这促进了对新陈代谢途径的理解,并有助于合成新分子和发现反应.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 系统生物学 系统生物学
背景情况:
- 代谢途径是生物体生物化学的基础.
- 路径模块,反应集群,包含关键化合物 (基质,中间体,产品).
- 识别化合物-通路模块关系对于分子合成和反应预测至关重要.
研究的目的:
- 开发一种用于预测复合路径模块关系的新计算方法.
- 解决现有方法的局限性,这些方法不能预测路径模块.
主要方法:
- 提出了一个深度学习模型,DeepRT.
- 集成的传递信息的神经网络 (MPNNs) 和变压器编码器.
- 利用分子图形结构来提取信息.
主要成果:
- 深度RT有效地提取全球和本地结构信息.
- 该模型执行两个任务:预测路径模块中的复合物存在,并对查询复合物-模块关系进行分类.
- 在专用数据集上,DeepRT的性能优于现有方法.
结论:
- 深度RT在预测化合物路径模块相互作用方面取得了重大进展.
- 该模型增强了对代谢途径组件的预测.
- 这促进了新型分子合成和以前未知的生物化学反应的发现.
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