MS2DeepScore - a novel deep learning similarity measure for mass fragmentation spectrum comparisons

Abstract

Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are considered characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of >100,000 mass spectra of about 15,000 unique known compounds, MS2DeepScore learns to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model's prediction uncertainty. On 3,600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and predicts Tanimoto scores with a root mean squared error of about 0.15. The prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. We demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity metrics have great potential for a range of metabolomics data processing pipelines.

Cross-Modal Retrieval between 13C NMR Spectra and Structures for Compound Identification Using Deep Contrastive Learning

“…To evaluate the extrapolation ability of CReSS, we assessed the dependence of identification accuracy on structural similarity to the training set. The Tanimoto coefficient based on ECFP4 fingerprints was employed as a measure of molecular similarity . As shown in Figure S7, molecules with a relatively high similarity to molecules in the training set had a higher recall@10 than molecules with relatively low similarity to molecules in the training set.…”

“…The Tanimoto coefficient based on ECFP4 fingerprints was employed as a measure of molecular similarity. 37 As shown in Figure S7, molecules with a relatively high similarity to molecules in the training set had a higher recall@10 than molecules with relatively low similarity to molecules in the training set. The results indicated that the similarity level of molecules in the external test set to those in the training set had a significant impact on the CReSS identification performance.…”

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

“…In fact, the first example of a supervised machine learning-based approach was just proposed. 46 These developments may further assist in the biochemical interpretation of such mass spectral networks thereby facilitating the process of turning large-scale untargeted mass spectral analyses into biochemical knowledge.…”

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

“…Additionally, Spec2Vec will enhance interpretation as it facilitates the correct assignment of metabolites, whose chemical class can be annotated and used to supplement metabolite annotation [237]. Despite its remarkable performance, Spec2Vec was not trained for the task of returning a higher similarity score for spectral pairs of structurally more closely related metabolites; therefore, very recently, MS2DeepScore was introduced that uses a Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra [238]. MS2DeepScore outperforms classical spectral similarity measures as well as Spec2Vec in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching.…”