ABCoRT:通过原子-键共学习对代谢物识别的保留时间预测
Guangbin Cheng1, Bingyi Wang2,3, Nannan Bai2,3
1School of Information Science and Engineering, Yunnan University, Kunming650091,China.
Journal of chemical information and modeling
|January 17, 2025
概括
准确预测代谢物保留时间对于非目标代谢学至关重要. 新的ABCoRT (Atom-Bond Co-learning for Retention Time) 模型增强了分子表示,以提高预测准确性和代谢物查.
科学领域:
- 计算化学是一种计算化学.
- 代谢学 代谢学 代谢学
- 机器学习 机器学习
背景情况:
- 在非目标代谢学中代谢物识别严重依赖于精确的液体染色学保留时间 (RT) 预测.
- 开发强大的分子表示是实现可靠RT预测的关键.
研究的目的:
- 引入ABCoRT (Atom-Bond Co-learning for Retention Time),这是一个新的框架,用于学习分子表示来预测代谢物保留时间.
- 评估ABCoRT在大型数据集上的性能及其在代谢物查中的实用性.
主要方法:
- ABCoRT 将分子图形转换为双重超图,用于协作更新原子和键信息.
- 该模型在小分子保留时间 (SMRT) 数据集 (80,038个分子) 上进行了评估,并在六个PredRet数据集上进行了微调.
- 在MetaboBASE和RIKEN_PlaSM数据集上进行了代谢物选.
主要成果:
- 在SMRT数据集上,ABCoRT实现了25.75秒的平均绝对误差 (MAE) 和3.24%的平均相对误差 (MRE).
- 微调的ABCoRT模型在6个PredRet数据集中的5个中获得了最低的MAE.
- 该模型有效过了MetaboBASE和RIKEN_PlaSM数据集中的候选化合物的38.35%和28.46%.
结论:
- ABCoRT为准确的保留时间预测提供了高度信息化的分子表示.
- 该框架显示了在代谢学研究中改善代谢物识别和查的巨大潜力.
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