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IDSL_MINT:一个深度学习框架,用于从质谱中预测分子指纹

Sadjad Fakouri Baygi¹, Dinesh Kumar Barupal²

¹Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, CAM Building, 3rd Floor, 17 E 102 St, New York, NY, 10029, USA.

Journal of cheminformatics

|January 18, 2024

概括

新的IDSL_MINT深度学习框架将质谱 (MS/MS) 光谱转化为分子指纹. 该工具增强了对非目标代谢学和暴露学数据的注释,改善了生物洞察力发现.

科学领域:

代谢学和暴露学.
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:

不针对的代谢学和暴露学研究产生了大量未注释的双重质谱 (MS/MS) 光谱,阻碍了生物解释.
多发性硬化/多发性硬化谱的结构性注释是从这些数据集中推进生物见解的重要瓶.

研究的目的:

引入IDSL_MINT,一个深度学习框架,旨在将MS/MS光谱转化为分子指纹描述器.
让用户使用自己的MS/MS光谱库和分子指纹来训练可定制模型.
改善MS/MS光谱在非向的代谢学和暴露学研究中的注释率.

主要方法:

开发了IDSL_MINT,这是一个深度学习框架,利用变压器模型进行质谱数据分析.
在用户提供的参考MS/MS库和可定制的分子指纹描述器上训练模型.
使用LipidMaps数据库和测试研究数据集进行基准标记IDSL_MINT.

主要成果:

IDSL_MINT成功地将MS/MS光谱翻译成分子指纹描述器.
该框架在与现有光谱库进行基准比较时,显示了以前没有注释的MS/MS光谱的注释率的提高.
使用LipidMaps数据库验证了性能.

结论:

IDSL_MINT提供了一种强大且可定制的解决方案,用于在代谢学和暴露学中的光谱注释.
该框架有可能显著提高大规模非定向研究中的总体注释率.
IDSL_MINT通过GitHub可用,促进可访问性和进一步开发.

关键词:

深度学习是一种深度学习.脂质地图脂质地图脂质地图利皮多米克 (Lipidomics) 是一种消化剂.质谱测量质量谱测量代谢学代谢学代谢学分子指纹描述器分子指纹描述器在PyTorch中使用PyTorch.变压器变压器变压器

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