dataesr/scientific-paragraphs-categorization huggingface dataset:
Molecular ngerprint-based metabolite annotation by CSI:FingerID In silico annotation of MS/MS spectra with molecular structures in the absence of reference spectra typically adopts one of two core strategies. In the rst, spectra predicted from candidate structures are compared with observed spectra. 24, 25 In the second, structural properties (normally molecular ngerprint vectors) are predicted from the observed spectra and compared with the same properties derived from candidate structures. Of these, the latter approach has historically outperformed the former. 26 Of the ngerprint-based approaches, CSI:FingerID, part of the SIRIUS soware package  27  is the state-of-the-art. CSI:FingerID starts with the computation of fragmentation trees from the MS/MS spectra in which each mass fragment becomes a tree node with the precursor ion as the tree origin and the connections in the fragmentation tree representing small chemical modications that are assumed to be part of fragmentation pathways occurring in the collision cell of the mass spectrometer.
e. , the presence/absence of an aromatic ring, presence/absence of a nitrogen atom, etc. From compound databases, having their structures in hand, the same ngerprint properties can be derived for the candidate structures obtained; and therefore, these candidates can be ranked according to the similarity of their ngerprint vectors with that predicted from the query spectrum. A key feature of SVMs in this context is that they do not explicitly use the fragmentation tree to make predictions, but rather the similarity between pairs of trees. That enables the combination of multiple similarity measures (via kernel functions) to allow for the leverage of different representations of the input data. Indeed, CSI:FingerID uses different types of structural information derived from the computed fragmentation trees for its predictions.
Chemical ontologies and taxonomies 3. 2. 1 ChEBI. Ontologies and taxonomies are of great value across many scientic disciplines as they help scientists to organize complex knowledge about concepts and to dene their relationships. Taxonomies are schemes that establish hierarchical classication of concepts or objects. Ontologies share the hierarchical structure of taxonomies, but oen allow for multiple relationship types and they introduce a formal naming of the types, properties and interrelationships of entities or concepts. One of the rst extensive structural ontologies that include chemical compound class annotations is part of the Chemical Entities of Biological Interest (ChEBI) database. 30 In their Chemical Ontology, the molecular structure sub-ontology classies molecular entities or parts thereof according to elemental composition and structure, e.
Thus, NPClassi-er  35  was proposed to link historical natural product classications to structures using deep learning. NPClassier converts the structure provided as a SMILES to a chemical ngerprint, then classies it with a deep neural network model built with 73 607 structures collected from public databases and the three-level ontology system organized as 7 pathways, 70 superclasses, and 653 classes dened based on literature search. Although this tool has been just introduced to the community, it is expected to enhance a number of computational biology pipelines for linking genome and metabolome datasets. Finally, it is presented as an open access tool to the community and novel natural product compound classes can easily be trained with sufficient available examples and added to the classication scheme.
The section ends with a brief overview of current tools that allow networking analysis on gas chromatography (GC-)MS datasets using electron impact ionization. This extends networking-based analyses to volatile measurements, another important group of molecules not covered by LC-MS metabolomics measurements. 4. 1. 1 Molecular networking. First introduced in 2012, 38 molecular networking (or more generically called mass spectral networking) has since become a popular tool in the analysis of MS/MS-based metabolomics data. 21, 39 The theoretical rationale of the method was quite straightforward. As fragmentation spectra acquired in MS/MS analyses are hypothesized to be related to their original chemical structures, molecules with similar structures will exhibit similar MS/MS spectra. Thus, if we can calculate spectral similarities between all spectra within a complex mixture, the spectral similarities can be extrapolated to the structural similarities between molecules in the mixture.
At rst, structural information on any molecular family member could be propagated into other family members. Network Annotation Propagation (NAP)  41  automates this process by exploiting this grouping to re-rank candidate structure annotations from compound databases considering the consistency of the structures within a molecular family: i. e. , if 8 out of 10 nodes have a avonoid glycoside as top ranked candidate, then the slightly lower ranked avonoid glycoside structures for the two remaining nodes are more likely to be the correct annotation than the top-1 ranked candidates that do not have structural resemblance to avonoids (see also Section 4. 2. 2). For an extensive review of the various tools that have been integrated within the GNPS platform in the context of natural products research, we refer to Fox Ramos et al.
Although mismatches between spectral similarity scores and the true structural similarities are frequently observed, little development of alternative scores has been undertaken. It is therefore very promising to see the recent development of a novel mass spectral similarity score based on an unsupervised machine learning approach inspired by the natural language processing algorithm Word2Vec,  44  called Spec2Vec. 45 Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13 000 unique molecules, it was demonstrated how Spec2Vec scores correlate better with structural similarity than cosinebased scores especially for structures that are not fully identical but share most of their structural features.
Consequently, a higher accuracy was reported for library matching experiments. Furthermore, the Spec2Vec score was used for mass spectral network analysis as well as large-scale analogue search where a large database is searched for structurally similar molecules based on their MS/MS spectra without using any parent mass ltering. To calculate the Spec2Vec scores, a mass spectral embedding needs to be learnt once. Subsequent mapping of new experimental MS/MS spectra on this embedding is then very fast. As a consequence, Spec2Vec returns structural analogues found in large databases within seconds, with particularly good results for molecules in the higher mass range (400-2000 Da) as shown for a GNPS library of >75 000 spectra with a cyclopeptide and lipid example. 45 We expect that Spec2Vec will trigger the emergence of more novel machine learning-based mass spectral similarity scores, both unsupervised and supervised, to further improve its performance for a range of diverse tasks such as the described library matching and analogue search, but for example also adduct annotation and mass spectral network creation tailored toward improved resolution for specic compound classes.
50)  compounds featuring an impressive array of structural variants which are divided into 42 representative skeletons. In addition, MIAs are also known for their various isomeric possibilities leading to analytical intricacies with respect to their annotation. To gain insight in how well network analyses reect thesometimes subtle-structural relationships of the MIAs, we created modied cosine (as used in GNPS) and Spec2Vec-based mass spectral networks. To illustrate the successes and the limitations of network analysis, 23 selected MS/MS spectra derived from the monoterpene indole alkaloid database (MIADB)  51  and belonging to seven representative skeletons (Fig. 3 ), were subjected to the classical molecular networking workow using two different modied cosine score thresholds (Fig. 3A  and B ) and two different Spec2Vec thresholds (Fig.
, how much query can be found in reference, or how much reference can be found in query). Other variations include the modied cosine (see Molecular networking section above) in which fragment peaks can be shied by the m/z difference of precursors, to account for simple chemical modications. Although spectral search is the most popular and reliable method for annotating metabolite features, challenges still exist. The most pressing is the relatively small size of the reference databases: only a very small subset of known chemicals are covered. In addition, although work has been done in this area,  [55] [56] [57] [58]  there is still no widely used method for computing false discovery rates for these data, which can make interpretation of cosine scores highly subjective without extensive manual inspection of spectral matches in terms of overlapping peaks and their intensity patterns.
4. 2. 2 Structure libraries for structural annotation. Although the number of mass spectra deposited in spectral libraries are growing fast, it is still much less than the total number of known chemicals. In silico fragmentation tools are one of the solutions to overcome this limitation of spectral library matching. By taking advantage of structural libraries, computational fragmentation tools increase the annotation rate in MS/MS-based metabolomics studies. 2  Since metabolite identication has been a bottleneck in untargeted metabolomics pipelines, various in silico tools have been developed: CSI:FingerID  59   As molecular networking has risen as one of the major methodologies in metabolomics, integration of in silico methods with molecular networking has been explored. ISDB-DNP is a hypothetical spectral database which was generated by CFM-ID  24  with structures from Dictionary of Natural Products; 61 and it was successfully integrated with molecular networking to provide metabolite annotations.
62 More recently, taxonomically informed scoring was suggested as a method for enhancing the condence of annotation using ISDB-DNP for natural product datasets. 63 Network Annotation Propagation (NAP) is the rst in silico annotation method which directly exploits the network topology provided by molecular networking. 41, 64 It re-ranks the candidate structures found by MetFrag  41, 64  based on the expected consistency of annotations within molecular families of connected components (see Section 3. 1). 4. 2. 3 MetWork. MetWork 65 was designed as a tool for specialized metabolite "anticipation". The main idea of anticipation is that all the molecules of a metabolome are connected by at least one chemical or biochemical transformation. 66, 67 It represents an implementation of the virtuous circle of metabolite identication  68  and is composed of three modules: a participatory database containing the available chemical transformations, a spectra prediction module based on CFM-ID  24  and a module for comparing predicted and experimental spectra allowing the annotation of molecules.
This realisation has driven the development of MolNetEnhancer. 36 MolNetEnhancer is a work-ow that combines the output from spectral library matching and in silico structural annotation onto a GNPS mass spectral molecular network. In this way it not only facilitates analysis, but also allows for powerful visualizations of the chemical diversity in the dataset. Chemical class annotation takes place by calculating the most predominant chemical classes retrieved for all top candidate matches per molecular family at each hierarchical level of the ClassyFire chemical ontology onto the network. Finally, substructure motifs (Mass2Motifs) learnt by MS2LDA that are shared between mass features of the same molecular family are visualized through additional edges connecting the nodes. Thus, instead of comparing tables and output from multiple platforms, information from mass spectral molecular networking, in silico structure annotation and substructure discovery can easily be visualized within one datale in Cytoscape.
Schroeder et al. used highresolution dqfCOSY to screen a library of bio-rationally selected insect extracts to manually generate libraries of structural fragments ("partial structures") 82 based on spin system connectivity. Further analysis of the data obtained of secretions from pupal Delphastus catalinae data revealed a large group of polyketide-like fragments. Satisfyingly, the subsequent database searches for these motifs and associated NMRspectroscopic data indicated that these compounds were unprecedented in nature. This in silico method leverages HMBC and HSQC spectra to extract short-range and long-range H-C correlations occurring in a carbon skeleton. Briey, an algorithm based on the community detection recovers individualized HMBC ngerprints from the HMBC data of a complex mixture.
Then, the collected H-C correlations are represented as a network of NMR peaks. Aer the generation of clusters from the obtained NMR peak network, molecular structures are assigned by means of an in-house theoretical HMBC and HSQC correlation database. Although this strategy has been exemplied for the identication of entire molecules, one can imagine the application of this technique to link the HMBC networking spectral features to substructures following a similar strategy but now linking the clusters to partial structures for example based on highly interlinked proton and carbon atoms. 5. 1. 4 Backbone topology determination. The Brüschweiler lab developed an elegant approach 85 based on a combination of a 13 C 2D-NMR technique (long time mixing time  13  C CT-TOCSY) and a deconvolution algorithm (DeCoDeC 86 ) that will identify traces that are unique for individual mixture components.
5. 3. 2 Atomic novelty scoring. Another 1 H-  13  C HSQC experiment-based method was suggested by Duggan et al. in 2019. 96 The authors established a 1 H-13 C HSQC database using publicly available spectra in the Human Metabolome Database (HMDB)  97  and the BioMagResBank. 98 As a result, the authors enlisted 10 308 1 H-  13  C HSQC peaks from 1207 spectra. These peaks were supposed to be common or usual peaks in general molecules. Then, the authors calculated Euclidean distances between all peaks in the proled spectrum (of single compound or crude extract) to the closest peak in the database. Since the 1 H or  13  C chemical shis represent the chemical environment of each atom in a molecule, this method can estimate the novelty of each atom of molecules; thus, this method can be used to prioritize a sample of interest or a target compound, which is expected to contain a novel substructure.
We do note that  13  C-NMR chemical shis are indeed well predicted using quantum mechanics (Hartree-Fock [HF] or density functional theory [DFT]) or machine learning (hierarchical organization of spherical environments [HOSE]). 103 The (further) automation and extension of the above-mentioned workows, in particular toward the use of 2D-NMR spectra, is hampered by the challenging process of 2D-NMR peak picking: existing methods are indeed based on the intensities or the area of the NMR peaks and oen fail in identifying peaks with low intensity or overlapping ones. 104 Here, we expect that the development of machine learning enhanced spectral feature recognition, for example based on computer vision (SCRV),  105  may play an important role in automated 1 H and  13  C NMR data extraction from experimental data, but also from NMR data presented in literature to further expand spectral databases.
Next to improved spectral feature recognition that will boost automated workows, we recognize a number of steps that the eld could take and work on to further this area. Firstly, the extension of NMR spectral libraries such as NMRShiDB 106 will help to map NMR signals to structures and their substructures and molecular ngerprints. The latter two can be used for automated chemical compound classication using ontologies such as ChemOnt (from ClassyFire) or NPClassier. Such extensions could also be done in silico  107  where the predictions are based on machine learning and incorporate an uncertainty measurement. When incorporated into NMR databases, data mining strategies could be used to link experimental and condently predicted NMR shi signals and patterns to chemical compound classes.
This could also result in augmented spectral databases and recently the PNMRNP database derived from UNPD was introduced to the community of natural product chemists. 108 In a similar spirit, the chemical shi prediction and matching capabilities of NMRlter at the structural level could be extended to the substructure level. 109 A related KnapsackSearch was also introduced as a database generator that provides taxonomically focused libraries of metabolites to narrow down the search space in a "bio-logical" manner. The importance of taxonomy in the identication of compound class was further highlighted in this recent contribution  110  by Rutz et al. where the authors present an open natural products database LOTUS (naturaL prOducTs occUrrences databaSe) in which the biological occurrences of over 500 000 natural product metabolites collected from various sources  111, 112  are now extensively documented.
Toward a database of annotated structural motifs The various examples provided here make clear that over the last two decades enormous steps have been taken in metabolomics analyses to go from le-by-le analysis toward integrated multi-le analyses of entire experiments. Several parts of the metabolomics analysis workow have been automated effectively such as preprocessing and noise ltering. However, metabolite feature recognition and metabolite annotation and identication remain bottlenecks as they still require substantial manual intervention and expertise. We argue here that machine learning approaches and network and substructurebased approaches are key strategies to come to the higher level of automated analysis needed to enable larger-scale metabolomics analysis compatible with the information-dense spectral data that current and the future instrumental advances will bring.
Machine learning approaches could be used to more accurately detect metabolite features in mass spectrometry and especially NMR data. The automated recognition of signals in 2D-NMR spectra is far from a solved challenge and with the increasingly available annotated datasets that will provide sufficient training and test material, the manual process of signal recognition could be automated. It is interesting to note that there is currently no central server to upload NMR spectra, and process, analyze, and visualize NMR results supported by a public spectral library,  119  whilst several of such platforms have emerged for mass spectrometry-based metabolomics data. 21, 120, 121 We do note that initiatives like nmrML  122  and NMReDATA  123  have been proposed which we believe will in turn spark the development of computational NMR-based metabolomics tools.
In mass spectrometry, the better discrimination between real metabolite features and artefact signals remains a topic of much attention and debate, with ion identity networking as one of the latest approaches to declutter the complex and dense mass spectral datasets. 124 Aer establishing a good peak list, the challenging structural annotation step begins. Here, the more efficient mining for unique substructure motifs will facilitate (i) the prioritization of novel chemistry in complex metabolite mixtures, and (ii) the structural elucidation procedure as such substructure motifs can oen be related to biosynthetic or chemical building blocks of the metabolites. In mass spectrometry, tools like MS2LDA  16  and MESSAR  23  have started to recognize substructure motifs based on spectral data, and MotifDB 20 is able to store annotated substructure motifs facilitating their reuse for future structural annotation purposes; however, in many cases, the structural annotation and verication of the substructure chemistry still relies on analytical experts that need to identify the structural motifs by linking them to elemental formulas and structures or chemical compound classes.
In NMR, 2D-NMR spectra have started to be exploited to recognize substructure motifs,  125  but here the automated recognition of NMR signals may hamper progress in this area. Looking into the future, we can envision the implementation of a database of MS and NMR annotated substructure motifs that can be used to populate the majority of metabolite features in your experiments with structural information supporting larger-scale metabolomics analyses (Fig. 5 ).
Pathway and taxonomy supported metabolite annotation Metabolites are seldomly present in isolation in complex mixtures, as they are typically linked to other metabolites through shared biochemical pathways or building blocks. To make effective use hereof, it is important to consider the biosynthetic capabilities of the organisms present in your experiment. For example, the concept of taxonomically informed scoring makes use of the presence and absence of metabolites across species and genera, thereby reranking candidate structure lists. 63 The whole idea of chemotaxonomy arises from the necessity for a living organism to produce a set of compounds linked to its gene pool. We thus believe the recent development of the well-curated compound databases NP Atlas 129 for microbial metabolites and LOTUS 110 for (mainly) plant-based metabolites are essential for the well-functioning of such approaches.
In that sense, the concept of differentially expressed metabolite sets (as enabled by PALS) that contain metabolites grouped based on their spectral similarity is an interesting exception; however, such metabolite sets do not necessarily represent biochemical pathways and further validation steps are thus required. Fig. 5  Technical advances on both the MS and NMR side both result in unprecedented chemical insights in complex metabolite mixtures. The recent surge in available computational metabolomics tools to extract substructure and chemical class information and to build networks based on spectral data is remarkable. Looking into the nearby future, we foresee an increased demand to perform large-scale repository-wide metabolomics analyses and the establishment of a database focused on substructure motifs that can be recognized in MS (MS/MS) and NMR (2D-NMR) data will be a key pillar to do so to annotate all the key building blocks in complex mixtures that form the bioactive components.
However, with the increase of publicly available data and the development of novel tools such as the new mass spectral similarity measure Spec2Vec as well as alternative networking-based approaches,  45, 134, 135  it could well be possible to group together structurally similar metabolites (according to historical reasons and/or biosynthetic routes) taking into account that mass spectral features that are not exactly similar could still be related to each other. Moreover, with an increased number of annotated datasets, supervised machine learning approaches could further improve on the current performance of Spec2Vec.