Machine Learning Enhanced Spectrum Recognition Based on Computer Vision (SRCV) for Intelligent NMR Data Extraction

Rapid and non-invasive detection of cystic echinococcosis in sheep based on serum fluorescence spectrum combined with machine learning algorithms

Cystic echinococcosis (CE) is a grievous zoonotic parasitic disease. Currently, the traditional technology of screening CE is laborious and expensive, developing an innovative technology is urgent. In this study, we combined serum fluorescence spectroscopy with machine learning algorithms to develop an innovative screening technique to diagnose CE in sheep. Serum fluorescence spectra of Echinococcus granulosus sensu stricto-infected group (n = 63) and uninfected E. granulosus s.s. group (n = 60) under excitation at 405 nm were recorded. The linear support vector machine (Linear SVM), Quadratic SVM, medium radial basis function (RBF) SVM, K-nearest neighbor (KNN), and principal component analysis-linear discriminant analysis (PCA-LDA) were used to analyze the spectra data. The results showed that Quadratic SVM had the great classification capacity, its sensitivity, specificity, and accuracy were 85.0%, 93.8%, and 88.9%, respectively. In short, serum fluorescence spectroscopy combined with Quadratic SVM algorithm has great potential in the innovative diagnosis of CE in sheep.

Advances in the Application of Artificial Intelligence-Based Spectral Data Interpretation: A Perspective

The interpretation of spectral data, including mass, nuclear magnetic resonance, infrared, and ultraviolet-visible spectra, is critical for obtaining molecular structural information. The development of advanced sensing technology has multiplied the amount of available spectral data. Chemical experts must use basic principles corresponding to the spectral information generated by molecular fragments and functional groups. This is a time-consuming process that requires a solid professional knowledge base. In recent years, the rapid development of computer science and its applications in cheminformatics and the emergence of computer-aided expert systems have greatly reduced the difficulty in analyzing large quantities of data. For expert systems, however, the problem-solving strategy must be known in advance or extracted by human experts and translated into algorithms. Gratifyingly, the development of artificial intelligence (AI) methods has shown great promise for solving such problems. Traditional algorithms, including the latest neural network algorithms, have shown great potential for both extracting useful information and processing massive quantities of data. This Perspective highlights recent innovations covering all of the emerging AI-based spectral interpretation techniques. In addition, the main limitations and current obstacles are presented, and the corresponding directions for further research are proposed. Moreover, this Perspective gives the authors' personal outlook on the development and future applications of spectral interpretation.

Total Synthesis and Absolute Configuration Determination of the α-Glycosidase Inhibitor (3S,4R)-6-Acetyl-3-hydroxy-2,2-dimethylchroman-4-yl (Z)-2-Methylbut-2-enoate from Ageratina grandifolia

Herein, we report the first total synthesis of α-glycosidase inhibitor (3R, 4S)-6-acetyl-3-hydroxy-2,2-dimethylchroman-4-yl (Z)-2-methylbut-2-enoate as well as its enantiomer. Our synthesis confirms the chromane structure separately proposed by Navarro-Vazquez and Mata, on the basis of DFT computations. Furthermore, our synthesis allowed us to determine the absolute configuration of the natural compound as (3S, 4R) and not (3R, 4S).

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

Library matching using carbon-13 nuclear magnetic resonance (¹³C NMR) spectra has been a popular method adopted in compound identification systems. However, the usability of existing approaches has been restricted as enlarging a library containing both a chemical structure and spectrum is a costly and time-consuming process. Therefore, we propose a fundamentally different, novel approach to match ¹³C NMR spectra directly against a molecular structure library. We develop a cross-modal retrieval between spectrum and structure (CReSS) system using deep contrastive learning, which allows us to search a molecular structure library using the ¹³C NMR spectrum of a compound. In the test of searching 41,494 ¹³C NMR spectra against a reference structure library containing 10.4 million compounds, CReSS reached a recall@10 accuracy of 91.64% and a processing speed of 0.114 s per query spectrum. When further incorporating a filter with a molecular weight tolerance of 5 Da, CReSS achieved a new remarkable recall@10 of 98.39%. Furthermore, CReSS has potential in detecting scaffolds of novel structures and demonstrates great performance for the task of structural revision. CReSS is built and developed to bridge the gap between ¹³C NMR spectra and structures and could be generally applicable in compound identification.

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

Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.