Introduction: Data Fusion-based Discovery (DAFdiscovery) is a pipeline designed to help users combine mass spectrometry (MS), nuclear magnetic resonance (NMR), and bioactivity data in a notebook-based application to accelerate annotation and discovery of bioactive compounds. It applies Statistical Total Correlation Spectroscopy (STOCSY) and Statistical HeteroSpectroscopy (SHY) calculation in their data using an easy-to-follow Jupyter Notebook.Method: Different case studies are presented for benchmarking, and the resultant outputs are shown to aid natural products identification and discovery. The goal is to encourage users to acquire MS and NMR data from their samples (in replicated samples and fractions when available) and to explore their variance to highlight MS features, NMR peaks, and bioactivity that might be correlated to accelerated bioactive compound discovery or for annotation-identification studies.Results: Different applications were demonstrated using data from different research groups, and it was shown that DAFdiscovery reproduced their findings using a more straightforward method.Conclusion: DAFdiscovery has proven to be a simple-to-use method for different situations where data from different sources are required to be analyzed together.

Peperomia pellucida is a species known in the Amazon as “erva-de-jabuti” that has been used in several therapeutic applications based on folk medicine. Herein, we describe the classes, subclasses, and the main compounds of the leaves, stems, and roots from P. pellucida by ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry associated with molecular networks, mirror plot on the GNPS library, and machine learning. These data show compounds that were annotated for the first time in the Peperomia genus, such as 2′,4′,5′-trihydroxybutyrophenonevelutin, dehydroretrofractamide C, and retrofractamide B.

Introduction Natural products and metabolomics are intrinsically linked through efforts to analyze complex mixtures for compound annotation. Although most studies that aim for compound identification in mixtures use MS as the main analysis technique, NMR has complementary advances that are worth exploring for enhanced structural confidence. Objective This review aimed to showcase a portfolio of the main tools available for compound identification using NMR. Materials and Methods COLMAR, SMART‐NMR, MADByTE, and NMRfilter are presented using examples collected from real samples from the perspective of a natural product chemist. Data are also made available through Zenodo so that readers can test each case presented here. Conclusion The acquisition of 1H NMR, HSQC, TOCSY, HSQC‐TOCSY, and HMBC data for all samples and fractions from a natural products study is strongly suggested. The same is valid for MS analysis to create a bridged analysis between both techniques in a complementary manner. The use of NOAH supersequences has also been suggested and demonstrated to save NMR time.

Este estudo descreve a aplicação e análise de uma aula problematizadora sobre a utilização e descarte inadequado de pilhas e baterias, tendo o objetivo de estimular a sensibilização ambiental em alunos do 9° ano de uma escola municipal de ensino público. Nesse sentido, a intervenção didática seguiu as seguintes etapas problematizadoras: (i) primeiramente, fez-se a investigação dos conhecimentos prévios dos discentes por meio da aplicação de um questionário; (ii) em seguida, desenvolveu-se uma aula expositivo-dialogada abordando características elementares envolvidas na temática proposta; (iii) depois, propôs-se uma pesquisa extraclasse envolvendo aspectos sustentáveis da utilização de pilhas e baterias e, por fim, (iv) seguiu-se de debates sobre os resultados apontados. Como resultado, constatou-se que os conhecimentos prévios dos alunos demonstraram pouca sapiência sobre o descarte desses resíduos e base conceitual acerca do assunto. Contudo, após a abordagem dialógica-problematizadora, percebeu-se o interesse dos alunos na temática a partir de evidenciações marcadas em seu dia a dia. As pesquisas extraclasse, por sua vez, demonstraram a percepção das consequências ambientais em detrimento do descarte inadvertido desses resíduos, levando a propostas de logísticas reversa, aplicação de multas à infratores, dentre outras. Com base isso, fez-se uma comparação dos novos conhecimentos apresentados, onde percebeu-se que o descaso apriorístico, por parte dos alunos, reverteu-se em um comprometimento na adoção de atitudes sustentáveis. Assim, considera-se que esta vertente estratégica atingiu o alcance de subsidiar uma aprendizagem suficiente para desenvolver a sensibilização ambiental sobre o uso e descarte corretos desses resíduos sólidos.

Introduction: This paper proposes DBsimilarity to organize structural databases into Similarity Networks to better understand the rich information available. Method: DBsimilarity was written in Jupyter Notebooks to be easy to follow and values readability. It converts SDF files into CSV files, adds chemoinformatics data, constructs a MZMine custom database file and a NMRfilter candidate list of compounds for rapid dereplication of MS and 2D NMR data, calculates similarities between compounds, and constructs CSV files to be converted to Similarity Networks using Cytoscape. Results: The Lotus database was used as source for Ginkgo biloba compounds and DBsimilarity was used to create Similarity Networks that includes NPClassifier classification to indicate biosynthesis pathways. Following, a database of validated antibiotics natural products was combined with the G. biloba database to indicate promising compounds. The presence of 11 compounds in both datasets points to a possible antibiotic property of G. biloba, and 122 other compounds similar to those known antibiotics is found. Next, DBsimilarity was used to filter the NPAtlas database (selecting only those with MIBIG reference) to identify potential antibacterial compounds using the ChEMBL database as reference. It was possible to promptly identify 5 compounds found in both databases, and 167 other worth investigating compounds similar to those known antibiotics. Conclusion: Chemical and biological properties are determined by molecular structures. DBsimilarity enables the creation of interactive Similarity Networks using Cytoscape. It is also in line with recent review that highlights significant sources of errors in compound identification: poor biological plausibility and unrealistic chromatographic behaviors.