Project Harvester: Improving molecular fingerprint prediction through self-training

小分子的快速注释在生物学和生命科学的许多领域都很有意义。质谱仪(MS)是从少量样品中注释小分子的一项关键技术。小分子的结构鉴定通常使用串联质谱仪(MS/MS)进行。MS/MS数据的计算分析是当今代谢组学和小分子研究的主要技术障碍之一。2015年，我的团队开发了CSI：FingerID，用于在分子结构数据库中搜索MS/MS数据。后来，我们开发了Canopus的化合物类的全面分配，而不需要结构说明。2021年，我们发布了宇宙工作流，使我们能够区分正确和不正确的注释。所有这些方法都依赖于MS/MS数据来训练底层机器学习模型。不幸的是，可用的参考MS/MS文库增长缓慢，而且比结构数据库或公开可用的生物数据慢得多。这个项目的基本目标是利用公开可用的生物数据来改进我们的机器学习模型。从小分子的MS/MS数据中预测分子指纹是许多计算方法的核心，如CSI：FingerID、Canopus和MS Novelist。该项目的目标是通过自我训练，利用公共资源库中可用的数十亿个小分子的未标记光谱，大幅提高指纹预测性能。我们将处理GNPS等储存库中公开可用的数十万次LC-MS/MS运行，找到高置信度注释，将这些注释的MS/MS光谱反馈到训练数据中用于指纹预测，并重复执行，直到收敛。我们项目的影响将是双重的。首先，我们可以提高所有依赖于指纹预测的方法的性能，包括CSI：FingerID，Canopus和MSNovelist。其次，我们的项目将为我们提供一个带有推测分子结构注释的大型MS/MS公共文库。这不仅允许其他人训练更好的机器学习模型(例如，竞争分段建模，CFM)，而且通常对计算方法开发也是有价值的。

Rapid annotation of small molecules is of interest in numerous areas of biology and the life sciences. Mass spectrometry (MS) is a key technology for the annotation of small molecules from small amounts of samples. Structural elucidation of small molecules is usually carried out using tandem mass spectrometry (MS/MS). Computational analysis of MS/MS data is one of the major technological hurdles in metabolomics and small molecule research today. In 2015, my group developed CSI:FingerID for searching MS/MS data in molecular structure databases. Later, we developed CANOPUS of the comprehensive assignment of compound classes without the need for structural elucidation. In 2021, we published the COSMIC workflow that allows us to differentiate between correct and incorrect annotations. All of these methods depend on MS/MS data to train the underlying machine learning models. Unfortunately, available reference MS/MS libraries are growing slowly, and much slower than structure databases or publicly available biological data. The fundamental objective of this project is to harness the publicly available biological data to improve our machine learning models. The prediction of molecular fingerprints from MS/MS data of small molecules lies at the heart of many computational methods such as CSI:FingerID, CANOPUS and MSNovelist. The goal of this project is to substantially improve fingerprint prediction performance through self-training, making use of the billions of unlabeled spectra from small molecules available in public repositories. We will process hundreds of thousands of LC-MS/MS runs publicly available in repositories such as GNPS, find high-confidence annotations, feed those annotated MS/MS spectra back into the training data for fingerprint prediction, and repeat until convergence. The impact of our project will be two-fold. Firstly, we can improve the performance of all methods that rely on fingerprint prediction, including CSI:FingerID, CANOPUS and MSNovelist. Second, our project will provide us with a large public library of MS/MS with putative molecular structure annotations. This will not only allow others to train better machine learning models (say, for Competitive Fragmentation Modeling, CFM) but also be of value for computational method development in general.