一种分子指纹预测方法,实现GATAT
Chengzhi Deng1,2, Chengli Zhou1, Lei Shi1
1Institute of Highland Forest Science, Chinese Academy of Forestry Kunming 650233 Yunnan P. R. China leishi@139.com.
RSC advances
|April 23, 2025
概括
一个新的图表注意网络 (GAT) 模型从质谱数据中准确预测分子指纹,改善了代谢学中的化合物识别. 这种方法的性能优于MetFID.ID等现有工具.
科学领域:
- 代谢学 代谢学 代谢学
- 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:
- 精确的化合物识别在代谢学中至关重要,但由于大量的代谢产物,具有挑战性.
- 现有的化合物识别方法在速度和准确性方面存在局限性.
研究的目的:
- 提出一种新的分子指纹预测方法,用于使用图形注意力网络 (GAT) 模型识别化合物.
- 与现有工具相比,评估拟议的基于GAT的方法的有效性和准确性.
主要方法:
- 处理来自双重质谱 (MS/MS) 数据的碎片树数据.
- 将自然语言处理启发的技术应用于碎片树图形数据.
- 用2层线性层训练3层GAT模型,用于分子指纹预测.
主要成果:
- 该GAT模型在预测MS/MS光谱的分子指纹方面表现出高精度,实现了优异的接收器操作特性 (ROC) 和精确回忆曲线.
- 与MetFID相比,该模型显示出更高的精度和F1得分,边缘特征被确定为关键性能影响因素.
- 对分子图书馆的验证显示,在前体质量查询方面,CFM-ID的性能与CFM-ID相当,在分子公式查询方面,MetFID的性能优于MetFID.
结论:
- 拟议的GAT模型提供了一种强大而准确的方法,用于代谢学中的化合物识别.
- 这项研究强调了图表注意力网络在促进代谢数据分析和化合物识别方面的潜力.
- 这些发现为未来的研究提供了基础,用于开发更复杂的代谢学计算工具.
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