Liquid Chromatography coupled to Tandem Mass Spectrometry (LC-MS/MS) based methods are currently the top choice for high-throughput, quantitative measurements of the proteome. While traditional proteomics LC-MS/MS methods can suffer from issues such as low reproducibility and quantitative accuracy due to its stochastic nature, recent improvements in acquisition protocols have resulted in methods that can overcome these challenges. Data-independent acquisition (DIA) is a novel mass spectrometric method that does so by using a deterministic acquisition strategy. These new approaches will allow researchers to apply MS on more complex samples, however, existing heuristic and expert-knowledge based methods will struggle with keeping pace of the increasing complexity of the resulting data. Deep learning (DL) based methods have been shown to be more adept at handling large amounts of complex data than traditional methods in many other fields, such as computer vision and natural language processing. Proteomics is also entering a phase where the size and complexity of the data will require us to look towards scalable and data-driven DL pipelines.
Mass spectrometry is a key tool in the study of small molecules, playing an important role in metabolomics, drug discovery, and environmental chemistry. Tandem mass spectra capture fragmentation patterns that provide key structural information about a molecule and help with its identification. Practitioners often rely on spectral library searches to match unknown spectra with known compounds. However, such search-based methods are limited by availability of reference experimental data. In this work we show that graph transformers can be used to accurately predict tandem mass spectra. Our model, MassFormer, outperforms competing deep learning approaches for spectrum prediction, and includes an interpretable attention mechanism to help explain predictions. We demonstrate that our model can be used to improve reference library coverage on a synthetic molecule identification task. Through quantitative analysis and visual inspection, we verify that our model recovers prior knowledge about the effect of collision energy on the generated spectrum. We evaluate our model on different types of mass spectra from two independent MS datasets and show that its performance generalizes. Code available at github.com/Roestlab/massformer.
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for
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