使用自回归神经网络生成分子碎裂图
Generating Molecular Fragmentation Graphs with Autoregressive Neural Networks
AI总结：
提出一种基于物理断裂事件的自回归神经方法，快速准确预测分子碎片及串联质谱，在精度、代谢物鉴定和可解释性上优于现有方法。
摘要：
从分子结构准确预测串联质谱有望通过扩充社区的实验参考标准库来解锁新的代谢组学发现。化学信息学质谱预测策略使用“断键”框架来迭代模拟质谱碎裂，但这些方法存在局限性：（a）速度慢，因为需要穷尽且组合性地断裂分子；（b）不准确，因为它们通常依赖启发式方法来预测每个生成碎片的强度。神经网络替代方案虽能降低计算成本，但属于黑盒模型且本质上并未提高准确性。我们引入了一种基于物理原理的神经方法，该方法能学习预测每个断裂事件，并快速准确地评估最相关的分子碎片子集。我们通过从公共和私有标准库预测质谱来评估我们的模型，结果表明，与以往的断裂方法和黑盒神经网络相比，我们的混合方法提供了最先进的预测精度、改进的候选数据库代谢物鉴定能力以及更高的可解释性。我们的方法基于物理碎裂事件，在阐明具有更复杂骨架的天然产物分子方面展现出极高的前景。
英文摘要：
The accurate prediction of tandem mass spectra from molecular structures has the potential to unlock new metabolomic discoveries by augmenting the community's libraries of experimental reference standards. Cheminformatic spectrum prediction strategies use a "bond-breaking" framework to iteratively simulate mass spectrum fragmentations, but these methods are (a) slow, due to the need to exhaustively and combinatorially break molecules and (b) inaccurate, as they often rely upon heuristics to predict the intensity of each resulting fragment; neural network alternatives mitigate computational cost but are black-box and not inherently more accurate. We introduce a physically-grounded neural approach that learns to predict each breakage event and score the most relevant subset of molecular fragments quickly and accurately. We evaluate our model by predicting spectra from both public and private standard libraries, demonstrating that our hybrid approach offers state of the art prediction accuracy, improved metabolite identification from a database of candidates, and higher interpretability when compared to previous breakage methods and black box neural networks. The grounding of our approach in physical fragmentation events shows especially high promise for elucidating natural product molecules with more complex scaffolds.
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