Ultra-fast and accurate electron ionization mass spectrum matching for compound identification with million-scale in-silico library

Spectrum−structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum−structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure−spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum−structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum−structure correlation soon, which would trigger substantial development of various disciplines.

Section: ■ Molecular Structure To Spectrummentioning

confidence: 99%

Deep Learning-Assisted Spectrum–Structure Correlation: State-of-the-Art and Perspectives

Lu,

Wu,

et al. 2024

“…One compound may generate distinct MS/MS spectra under different collision energies, , dissociation strategies, and mass tolerance windows . On the other hand, compounds having very different structures may also generate similar MS/MS spectra. , To better establish linkages among compounds, it is necessary to complement existing molecular networks with rules independent of MS spectra.…”

Section: Introductionmentioning

confidence: 99%

Compound Similarity Network as a Novel Data Mining Strategy for High-Throughput Investigation of Degradation Pathways of Organic Pollutants in Industrial Wastewater Treatment

An,

Chen,

Zhang

et al. 2024

Identification of degradation products and pathways is crucial for investigating emerging pollutants and evaluation of wastewater treatment methods. Nontargeted analysis is a powerful tool to comprehensively investigate the degradation pathways of organic pollutants in real-world wastewater samples but often generates large data sets, making it difficult to effectively locate the exact information on interests. Herein, to efficiently establish the linkages among compounds in the same degradation pathways, we introduce a compound similarity network (CSN) as a novel data mining strategy for LC−MS-based nontargeted analysis of complex wastewater samples. Different from molecular networks that cluster compounds based on MS/MS spectra similarity, our CSN strategy harnesses molecular fingerprints to establish linkages among compounds and thus is spectra-independent. The effectiveness of CSN was demonstrated by nontargeted identification of degradation pathways and products of organic pollutants in leather industrial wastewater that underwent laboratory-scale activated carbon adsorption (ACD) and ozonation treatments. Utilizing CSN in interpreting nontargeted data, we tentatively annotated 4324 compounds in the untreated leather industrial wastewater, 3246 after ACD, and 3777 after ACD/ozonation. We located 145 potential degradation pathways of organic pollutants in the ACD/ozonation process using CSN and validated 7 pathways with 15 chemical standards. CSN also revealed 5 clusters of emerging pollutants, from which 3 compounds were selected for in vitro cytotoxicity study to evaluate their potential biohazards as new pollutants. As CSN offers an efficient way to connect massive compounds and to find multiple degradation pathways in a high-throughput manner, we anticipate that it will find wide applications in nontargeted analysis of diverse environmental samples.

“…Directed message passing neural networks (D-MPNN) operate on molecular graphs based on chemical structures in the simplified molecular input line entry system (SMILES) notation to build neural representations of molecules for property prediction including mass spectra. Notably, a recent study by Yang et al addresses limitations in reference library coverage by predicting additional missing mass spectra with known structures using NEIMS.…”

Section: Introductionmentioning

confidence: 99%

PS²MS: A Deep Learning-Based Prediction System for Identifying New Psychoactive Substances Using Mass Spectrometry

Lin,

Chien,

Wang

et al. 2024

The rapid proliferation of new psychoactive substances (NPS) poses significant challenges to conventional mass-spectrometry-based identification methods due to the absence of reference spectra for these emerging substances. This paper introduces PS 2 MS, an AI-powered predictive system designed specifically to address the limitations of identifying the emergence of unidentified novel illicit drugs. PS 2 MS builds a synthetic NPS database by enumerating feasible derivatives of known substances and uses deep learning to generate mass spectra and chemical fingerprints. When the mass spectrum of an analyte does not match any known reference, PS 2 MS simultaneously examines the chemical fingerprint and mass spectrum against the putative NPS database using integrated metrics to deduce possible identities. Experimental results affirm the effectiveness of PS 2 MS in identifying cathinone derivatives within real evidence specimens, signifying its potential for practical use in identifying emerging drugs of abuse for researchers and forensic experts.