学习动机的特征和分子的拓结构用于代谢途径预测
Jianguo Hu1, Yiqing Zhang1, Jinxin Xie1
1Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
Journal of cheminformatics
|April 21, 2025
概括
使用motif,图形神经网络和3D数据,MotifMol3D可以预测未知的代谢物的代谢途径类别. 这种计算工具提高了代谢分析的准确性和解释性.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 化学信息学 化学信息学
背景情况:
- 代谢物是疾病进展的关键生物标志物.
- 识别代谢途径至关重要,但具有挑战性,特别是对于未知的代谢物类别.
- 当前的计算方法往往缺乏可解释性.
研究的目的:
- 开发一个可解释的计算框架来预测分子代谢途径类别.
- 使用新的特征提取技术,提高代谢途径预测的准确性.
主要方法:
- 开发了MotifMol3D,这是一个神经网络框架,集成了图形信息,图形神经网络 (GNN) 和3D结构数据.
- 利用了11个KEGG代谢途径类别中的5,698个分子的数据集.
- 纳入动机分析来挖掘当地的分子特征并提高可解释性.
主要成果:
- 在精度,回忆和F1评分方面,MotifMol3D的性能超过了最新的方法,用于预测代谢途径.
- 废弃性研究证实了该模型的有效性.
- 动机分析表明,动机信息对特定途径分子具有特征,并提高了模型的可解释性.
结论:
- MotifMol3D为预测代谢途径类别提供了准确和可解释的解决方案.
- 模式信息的整合为代谢途径中的分子性质提供了新的见解.
- 该开源工具可用于更广泛的研究应用.
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