Tandem mass spectrum prediction for small molecules using graph transformers

Young, Adamo; Röst, Hannes; Wang, Bo

doi:10.1038/s42256-024-00816-8

Cited by 7 publications

(1 citation statement)

References 60 publications

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“…The introduction of the transformer in 2017 has revolutionized deep learning, particularly in natural language processing, computer vision, and biological sequence modeling . Recent studies, such as Graphormer and structure-aware transformer (SAT), have successfully applied graph transformer models to molecular representation learning and property prediction, achieving performance superior to traditional methods. , Moreover, graph transformer models have found broad applications in other fields: RTMScore for protein–ligand binding and virtual screening, MOFNet for predicting adsorption isotherms in metal–organic frameworks, and MassFormer for tandem mass spectrum prediction of small molecules, among others. − These advancements underscore the potential of combining transformers with GNNs for a variety of scientific tasks.…”

Section: Introductionmentioning

confidence: 99%

Integrating Prior Chemical Knowledge into the Graph Transformer Network to Predict the Stability Constants of Chelating Agents and Metal Ions

Chen,

Qin,

Sheng

2024

J. Chem. Inf. Model.

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The latest advancements in nuclear medicine indicate that radioactive isotopes and associated metal chelators play crucial roles in the diagnosis and treatment of diseases. The development of metal chelators mainly relies on traditional trialand-error methods, lacking rational guidance and design. In this study, we propose the structure-aware transformer (SAT) combined with molecular fingerprint (SATCMF), a novel graph transformer network framework that incorporates prior chemical knowledge to construct coordination edges and learns the interactions between chelating agents and metal ions. SATCMF is trained on stability data collected from metal ion−ligand complexes, leveraging the SAT network to extract structural features relevant to the binding of ligands with metal ions. It further integrates molecular fingerprint features to refine the prediction of the stability constants of the chelating agents and metal ions. The experimental results on benchmark data set demonstrate that SATCMF achieves state-of-the-art performance based on four different graph neural network architectures. Additionally, visualizing the learned molecular attention distribution provides interpretable insights from the prediction results, offering valuable guidance for the development of novel metal chelators.

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Section: Introductionmentioning

confidence: 99%

Integrating Prior Chemical Knowledge into the Graph Transformer Network to Predict the Stability Constants of Chelating Agents and Metal Ions

Chen,

Qin,

Sheng

2024

J. Chem. Inf. Model.

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Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening

Hupatz,

Rahu,

Wang

et al. 2024

Anal Bioanal Chem

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Non-targeted screening with liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS) is increasingly leveraging in silico methods, including machine learning, to obtain candidate structures for structural annotation of LC/HRMS features and their further prioritization. Candidate structures are commonly retrieved based on the tandem mass spectral information either from spectral or structural databases; however, the vast majority of the detected LC/HRMS features remain unannotated, constituting what we refer to as a part of the unknown chemical space. Recently, the exploration of this chemical space has become accessible through generative models. Furthermore, the evaluation of the candidate structures benefits from the complementary empirical analytical information such as retention time, collision cross section values, and ionization type. In this critical review, we provide an overview of the current approaches for retrieving and prioritizing candidate structures. These approaches come with their own set of advantages and limitations, as we showcase in the example of structural annotation of ten known and ten unknown LC/HRMS features. We emphasize that these limitations stem from both experimental and computational considerations. Finally, we highlight three key considerations for the future development of in silico methods. Graphical Abstract

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Knowledge-based in silico fragmentation and annotation of mass spectra for natural products with MassKG

Zhu,

Li,

Jin

et al. 2024

Computational and Structural Biotechnology Journal

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Tandem mass spectrum prediction for small molecules using graph transformers

Cited by 7 publications

References 60 publications

Integrating Prior Chemical Knowledge into the Graph Transformer Network to Predict the Stability Constants of Chelating Agents and Metal Ions

Integrating Prior Chemical Knowledge into the Graph Transformer Network to Predict the Stability Constants of Chelating Agents and Metal Ions

Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening

Knowledge-based in silico fragmentation and annotation of mass spectra for natural products with MassKG

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