MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra

Abstract

The ‘inverse problem’ of mass spectrometric molecular identification (‘given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came’) is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem (‘calculate a small molecule’s likely fragmentation and hence at least some of its mass spectrum from its structure alone’) is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the ‘translation’ a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the ‘true’ molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are ‘similar’ to the top hit. In addition to using the ‘top hits’ directly, we can produce a rank order of these by ‘round-tripping’ candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower, including those in the last CASMI challenge (for which the results are known), getting 49/93 (53%) precisely correct. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. It seems to act as a Las Vegas algorithm, in that it either gives the correct answer or simply states that it cannot find one. The ability to create and to ‘learn’ millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.

Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome

“…Our study highlighted several other compounds (not discussed here) that changed significantly in severity and outcome. However, as is common in metabolomic studies (Blaženović et al, 2018;Salek et al, 2013;Shrivastava et al, 2021), these require further rigorous identification following the Metabolomics Standards Initiative (MSI) for reporting metabolite identification (Sumner et al, 2007) and so were not included in the predictive model at this stage. Examples of such compounds include pentahomomethionine (MSI 3) and trihomomethionine (MSI 3), both sulphurcontaining amino acids.…”

Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools

“…Here, mass fragmentation spectra (MS/MS spectra) acquired through data dependent acquisition (DDA) or data independent acquisition (DIA) alternatives have demonstrated their merits in adding structural information to metabolomics profiles, as we will also demonstrate throughout this review. As such, computational metabolomics tools that capitalize on MS and MS/MS information are a pragmatic solution since it is unlikely that we will ever cover the true chemical diversity in nature exhaustively with available reference standards given the vastness of estimated natural chemical space (Polishchuk et al, 2013 ; Shrivastava et al, 2021 ).…”

“…This, of course, strongly relies on the quality of the generated data, i.e., how closely in-silico generated MS/MS spectra correspond to actual MS/MS spectra of the respective molecules. For instance, the usability of transformer-based DL architectures for doing mass spectral annotations was recently demonstrated with MassGenie (Shrivastava et al, 2021 ). To overcome the limitation of low amounts of metabolomics data, the authors of MassGenie used in-silico fragmentation to generate MS/MS spectra for about 6 million small molecules.…”

“…MassGenie is one of the first approaches to demonstrate what this might eventually look like. It uses a transformer architecture that is trained using 6 million in-silico generated spectra (Shrivastava et al, 2021 ). Not unexpectedly based on our previous considerations, it turns out that this model does not generalise well enough to be used for broad-scale structure prediction.…”

The critical role that spectral libraries play in capturing the metabolomics community knowledge

“…CSI:FingerID (Dührkop et al, 2015), COSMIC (M. A. Hoffmann et al, 2021), MassGenie (Shrivastava et al, 2021)).…”