dataesr/scientific-paragraphs-categorization huggingface dataset:
These reactions are catalyzed by enzymes that have broad substrate specificity, such as CYP450s and UGTs. To handle these situations, new reaction subtypes and constraints were defined, which focused on a specific subclass of compounds that fulfilled a defined set of structural constraints. The resulting manually generated rules were then subject to further testing and validation. An example of such a reaction/rule is the N-dealkylation of alicyclic tertiary amines catalyzed by CYP3A4, a wellstudied bioactivation pathway of cyclic amines  [72]. In addition to the core knowledge provided by textbooks, online databases and journal articles, the design of biotransformation rules for the reaction knowledgebase often required additional investigation. One approach consisted of selecting compounds (from MetXBioDB) that triggered a given reaction and labeling them based on whether their expected metabolites were reported or not.
The reasoning engine The BMPT's Reasoning Engine uses the rules in the reaction knowledgebase to select the most likely of all applicable metabolic biotransformations or pathways. In general, two types of reasoning are used for the selection and ranking of predicted metabolites: absolute reasoning, and relative reasoning  [77]. Absolute reasoning solely focuses on the likelihood of a biotransformation to occur, and is used to select the biotransformations with an occurrence ratio above a given threshold. Examples of biotransformation software using absolute reasoning include SyGMA and Meteor Nexus. Relative reasoning evaluates the comparative likelihood between two independent but competing reactions (e. g. flavone 7-O-demethylation is more likely to occur than flavone 4′-O-demethylation  [74].
For the prediction of enzyme-substrate specificity, the current version of Bio-Transformer focuses on nine of the most "active" or beststudied CYP450 enzymes (CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C18, CYP2D6, CYP2E1, and CYP3A4). The prediction of their specificity toward a given substrate is made by CypReact  [57]  a machine learning software tool for CYP450 reaction prediction that was recently developed by our team. To predict whether a compound is hydrophilic enough for conjugation/elimination, BioTransformer uses its internal, machine learning Phase II filter that use structural fingerprints, and physicochemical properties (e. g. LogP, mass) to select likely Phase II candidates. CypReact, and the Phase II filter will be briefly described in the next section. With the reaction knowledgebase and the machine learning tools in hand, the Reasoning Engine was implemented programmatically for each of the five different transformer modules.
The enzyme-specificity is assessed by a program called Cyp-React  [57]. CypReact is a software tool that uses a machine learning approach to predict whether a small molecule reacts with any of the nine major CYP450 isozymes. CypReact uses a random forest model for each of seven isozymes (CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C19, CYP2E1, CYP3A4), and ensemble models for two isozymes (CYP2C9, CYP2D6). Each of the models uses a set of physicochemical properties and structural features of a molecule for substrate specificity prediction. The substructure fingerprints were partly developed by including a subset of SMARTS pattern definitions from ClassyFire  [67] , and a set of SMARTS patterns known to trigger CYP450-catalyzed metabolism (e. g. p-substituted phenols, or N-substituted piperazine). These fingerprints encode other pattern definitions for key functional groups and structural features relevant to CYP450-catalyzed metabolism, which were obtained through data mining.
These include inorganic compounds, and several classes of glycero-and glycerophospholipids, among others. CypReact is freely available at https ://bitbu cket. org/Leon_Ti/cypre act/. Given any small molecule, the CYP450 transformer uses CypReact to predict which of the nine CYP450s is likely to metabolize the molecule. Subsequently, it implements the constraints and biotransformation rules encoded within the reaction knowledgebase to predict the structures of the resulting metabolites. As for any other transformer module, the user can vary the parameters, including the number of transformation steps, and whether to use certain precedence rules.
In contrast to CypReact, which combines nine independent predictors (one for each CYP450 isozyme), the P2F consists of a single machine learning model. Because of the broad specificity of the aforementioned Phase II enzymes, especially UPD-GTs and SULTs, it was important to collect as structurally diverse a set as possible. Selected compounds included xenobiotics (e. g. pharmaceuticals, pesticides, food additives, toxins, phytochemicals), as well as endobiotics (e. g. steroids, bile acids, amino acids). A total of 1113 compounds were collected from several databases, including MetXBioDB, PubChem  [79] , BRENDA  [69] , and the Cyc databases  [70] , as well as the scientific literature. The training set contained 807 Phase II substrates, and 306 Phase II nonsubstrates. When unavailable from any of the sources, the structure of a compound was generated using Che-mAxon's MarvinSketch v.
g. the number of H-bonds, the mass, and the AlgoP), as well as 23 structural features, such as amine groups (SMARTS = "[NX3+0,NX4+;!$([N]~[! #6]);!$([N]*~[#7,#8,#15,# 16 ])]"), and carboxyl groups (SMARTS = "[#8;A;X2H1,X1-][#6]([#6,#1;A])=O"). The molecular descriptors were all computed with the CDK library. The structural features are represented as binary features in a custom chemical fingerprint to encode their absence (0) or presence  [1]  in the query molecule. A list of structural features and physicochemical parameters is available in Additional file 3: Table  S1. Feature selection was performed to select a set consisting of the features that are most significant in explaining the training data. This not only accelerated the training/ prediction process but also reduced the likelihood of overfitting.
Therefore, the comparison of spectral features (e. g. mass, molecular formula) obtained from mass spectra with those obtained from metabolism prediction data could help to putatively identify known or unknown metabolites and validate predictions. The BioTransformer metabolite identification tool (BMIT) is an additional module within BioTransformer that is designed to assist users in metabolite identification. It relies on the BMPT to find compounds of a specific mass (within a user-specified threshold) or chemical formula that are generated upon single-or multistep metabolism of a given parent molecule. BMIT takes the chemical structure of the starting molecule as input, as well as a list of neutral chemical masses or molecular formulas for the metabolites to be identified. BMIT is implemented to only support metabolite identification using the allHuman and superbio options (Human + Human Gut Microbiome), or the envmicro option (Environmental Microbiome).
BioTransformer's input and workflow BioTransformer was implemented in the Java programming language, and can be used as a command-line tool (on Linux, Mac OSX, and Windows) to perform metabolism prediction and metabolite identification of small molecules. Beside CypReact, described earlier, BioTransformer uses two other open source tools, namely the Chemistry Development Kit (CDK)  [83]  and the AMBIT library  [84]. The CDK programming library is used for several operations, including the calculation of physicochemical properties, the execution of superstructure search operations, and the handling of chemical structures, among others. The AMBIT library is used for the application of biotransformation rules and structure generation. The BioTransformer metabolism prediction tool's workflow is illustrated in Fig.
The BioTransformer web service The BioTransformer software package can be used as a command line tool or as a Java library. In order to further facilitate access to this tool, a RESTful web service was built using the JRuby on Rails framework. The Bio-Transformer web service is freely available at www. biotr ansfo rmer. ca. The web service allows users to manually or programmatically submit queries, and retrieve the corresponding results using the workflow described in the previous section. In particular, the web service allows users to submit compounds in SMILES, InChI, and SDF formats (Additional file 2: Fig. S7 ). Query results can be returned as JSON, SDF, and CSV documents (Additional file 2: Fig. S8 ). Moreover, the web server provides information about each previously predicted single-step metabolic transformation of the compound, including the corresponding biosystem, reaction type, metabolizing enzymes, and transformation products.
The web application offers several advantages compared to the command-line tool, namely: (1) it is easier to use than the stand-alone program; (2) users need not be programmers or need to install a local program to run the web service; (3) several queries can be processed simultaneously;  (4)  the computation is faster, as previous prediction results are saved in a database to facilitate more rapid retrieval; and (5) metabolite prediction and identification data can be accessed manually or programmatically and downloaded in several formats. While the command-line executable does not benefit from the database of computed metabolites, it also does provide some advantages, namely:  [1]  it allows users to submit large sets of compounds;  [2]  it does not rely on an Internet connection, and;  [3]  queries are executed immediately and not put in a queue.
Evaluation of BioTransformer's metabolism prediction and metabolite identification capabilities In order to evaluate the performance of BioTransformer, we performed a comparative analysis with two popular in silico metabolism prediction tools, namely Meteor Nexus  [26] , and ADMET Predictor  [29]. Moreover, we evaluated BioTransformer's ability to replicate environmental microbial metabolism prediction from the EAWAG BDD/PPS system  [33, 34, 73]. We also tested BioTransformer's ability to predict comprehensive human and gut metabolism of small molecules. Building on BioTransformer's metabolism prediction ability, we also tested its metabolite identification capabilities with the BMIT module. For each of the tests, BioTransformer was run on a 2. 7 GHz Intel Core i5 MacOSX with 16 GB (1867 MHz DDR3) of memory.
Comparative Evaluation of BioTransformer and ADMET Predictor in the Prediction of Human Single-step CYP450-mediated Metabolism of Small Molecules In our third test, the CYP450-catalyzed single-step metabolism of the 60 aforementioned molecules was predicted using ADMET Predictor (v. 8. 5. 1. 1) [29]. ADMET Predictor is a software tool that allows the prediction of sites of metabolism and the resulting metabolites  upon CYP450-catalyzed biotransformation. The set of nine CYP450 isoforms supported by ADMET Predictor (1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4) is identical to the one covered by BioTransformer CYP450 metabolism prediction tool. The resulting metabolites were compared to those obtained from BioTransformer's CYP450 metabolism prediction module, and a performance assessment was then carried out (Table  4 ).
The aim of this comparison was to assess the ability of BioTransformer to reproduce the EAWAG-BBD/PPS predictions, since the rules applicable to environmental degradation were encoded using the freely accessible EAWAG Biodegradation and Biocatalysis database. Both BioTransformer and the EAWAG-BBD/PPS system were set to apply relative reasoning, and both were set to predict all microbial transformations (i. e. aerobic and anaerobic). BioTransformer was able to replicate all 15 biotransformations predicted by the EAWAG system, and to successfully predict all 18 metabolites predicted by EAWAG. In addition, BioTransformer predicted three more metabolites for the degradation of Disulfoton. All three metabolites resulted from the correctly used biotransformation rule (bt0259), which was applied at three different sites of metabolism, producing two metabolites in each case.
Spot urines were sampled after the supplementation period and compared to the spot urines sampled under the same conditions after 9 days of the same diet without epicatechin. The samples were analysed by high-resolution mass spectrometry-UPLC-QToF (Bruker, Impact II), with the mass spectrometer operated in the positive ion mode. More detailed information about the specific experimental protocols, the treatment protocols and the mass spectral data extraction/analysis is provided in Additional file 2. In order to identify the metabolites observed in our study, the BMIT module used a set of 260 neutral monoisotopic masses, derived from the [M + H] + ions extracted from the experimental QToF MS data collected from the rat urine samples, ranging from 53. 4896 to 969. 8669 Da. Monoisotopic masses were generated by subtracting 1.
00727 Da from the ions extracted from the MS dataset. Details regarding the data extraction process are provided in Additional file 2. These masses exhibited marked increases in intensity after epicatechin supplementation compared to baseline. The human supertransformer (option superbio) was used to facilitate putative compound identification. From the 260 monoisotopic masses that were extracted, BMIT identified 37 possible metabolites of epicatechin corresponding to 20 unique masses. These masses do not correspond to adducts or Fig. 8  Environmental microbial metabolism of disulfoton, as predicted by BioTransformer and the EAWAG-BBD/PPS system. The metabolites BTM0004, BTM0006, and BTM0009 are reported by BioTransformer as by-products of the biotransformation bt0259 that generate BTM0003, BTM0005, and BTM0010.
BioTransformer's design and implementation BioTransformer is a software tool that combines both a knowledge-based approach and a machine learning approach to predict the metabolism of small molecules, and to assist in metabolite identification. The knowledge-based system consists of a biotransformation database (MetXBioDB), a knowledgebase (the reaction knowledgebase), and a reasoning engine. MetXBioDB is a unique resource that is freely available, and covers a wide range of enzymatic reactions that take place in human tissues, the human gut and the environment (soil and water microflora). In contrast to most publicly available databases, MetXBioDB provides detailed biological and chemical information about all of its biotransformations, including the catalyzing enzymes, the substrates, the products, and the biotransformation rule(s) that is/are applied.
MetXBioDB describes the metabolism of > 2000 compounds catalyzed by ~ 15 enzyme families. For each biotransformation, at least one scientific source or reference is provided. MetXBi-oDB is stored as a JSON document, which can be easily parsed. One potential application of MetXBioDB is in the design of biotransformation rules with narrow specificity, which can be used for in silico metabolism prediction. In fact, this resource has already been used (in addition to other data) to successfully design > 300 biotransformation rules, which were used to annotate the biotransformations in the database and predict metabolites via the BioTransformer Reasoning Engine. Despite the aforementioned strengths of MetXBioDB, the database still has a number of limitations. Although it covers a large number of enzymatic reactions, it is clear that more data is needed in order to cover an even larger set of reactions (e.
g. oxidation reactions) catalyzed by enzymes other than CYP450s. It is also clear that there is a need to define more constraints and/or build additional models that would increase the quality of the predictions. Moreover, users could benefit from data about the different sites of metabolism for each specific biotransformation, as it would serve as a training set for the development of models for the prediction of sites of metabolism (SoMs). For the current version of MetXBioDB, the intent was simply to provide an easily readable and comprehensible data set. However, providing MetXBioDB in a database format that can be parsed and queried in a more sophisticated way (e. g. SQL) would make the database much more useful to a broader number of users. Efforts are underway to do so for the next release of MetXBioDB.
Evaluation of BioTransformer's predictions In our first test, BioTransformer was evaluated against Meteor Nexus (v. 3. 0. 1). Meteor Nexus is a commercially available software tool that is considered to be the gold standard for predicting biotransformations of xenobiotics. While BioTransformer achieved a better prediction (49%) and recall (88%) than Meteor Nexus at the equivocal level of confidence (35% precision, and 71% recall), Meteor Nexus' precision improved significantly at the plausible (56%), and probable (59%) levels. The increase in Meteor Nexus' precision matched our expectations, as the minimum likelihood threshold for metabolite selection increased, thus reducing its probability of selecting unconfirmed metabolites. However, the 68% increase in precision (from Equivocal to Probable) led to an 82% decrease in recall.
In a fourth test, we evaluated BioTransformer's ability to identify metabolites using its BMIT module. This task tacitly relies on the metabolism prediction task, and Bio-Transformer was able to suggest 37 metabolites matching 20 masses from a list of 260 monoisotopic masses extracted from the MS analysis of urine samples collected after exposure to epicatechin (Additional file 7: Table  S1 ). Of those, 18 metabolites were identified as previously known metabolites. Twenty-six monoisotopic masses matching to 36 reported epicatechin metabolites were not observed in our experimental study. This variation in the observed metabolites may be caused by different experimental settings and analytical conditions (e. g. length of the treatment, species, gender, dietary background, sample preparation and analysis methods) in different studies.
For example, rats are expected to perform less sulfonation of epicatechin than humans  [87]. In a second run, BMIT was used to search metabolites corresponding to monoisotopic masses that were observed in previous studies but not in our experimental dataset. In this test it was able to correctly identify another 21 known epicatechin metabolites. Overall, BMIT was able to predict 39 out of 56 previously reported compounds. The discrepancy between the number of metabolites suggested by BMIT and the number of previously reported metabolites could be explained by several factors. First, ten of the known epicatechin metabolites not predicted by BMIT (3 masses observed in our study) are products of a 2-step conjugation, but the superbio option simulates only one conjugation step, as it is often sufficient to make a molecule stable and hydrophilic enough for excretion (based on experimental data from MetXBioDB).
Thorough identification of the observed metabolites using MS/MS or authentic (synthesized) standards was not performed in our assessment of the metabolites present in urine. Epicatechin is metabolized in the liver, and more extensively by the gut microbiome. The ability of BMIT to identify/predict both human and human microbial epicatechin metabolites suggests that this module would be a useful asset in elucidating the dark matter in hostmicrobiome metabolomics  [88]. BMIT should also be a very useful tool for general metabolism prediction and metabolite identification using MS or MS/MS data. In addition, the predictions generated by BMPT could be very useful for suspect-screening analysis, and thereby permit faster non-targeted data analysis and more facile putative compound identification.
Additional biotransformation data would also provide further statistical evidence to fine tune the reaction preference rules (relative reasoning) and occurrence ratios for absolute/relative reasoning. In particular, adding an option for absolute reasoning would give BioTransformer the ability to select candidates with a set cut-off score. Currently BioTransformer's biotransformation database (MetXBioDB) and its reaction knowledgebase cover only a small portion of gut microbial metabolism (i. e. metabolism of plant-derived polyphenols). As many xenobiotics as well as endogenous compounds are known to be metabolized in the gut  [75, [89] [90] [91] [92] , it will be important to further expand the coverage of gut microbial metabolism in BioTransformer. We plan to make these improvements in upcoming versions of BioTransformer.
Within mammals, we have shown that BioTransformer was able to accurately predict single-step biotransformations for a diverse set of xenobiotics, including drugs, pesticides, and food compounds. The reactions that BioTransformer predicts cover Phase I and Phase II metabolism in mammals, as well as the human gut microbial metabolism. Overall, BioTransformer was shown to perform better than Meteor Nexus and ADMET Predictor, two highly regarded commercial software tools for in silico metabolism prediction. Unlike most other metabolic prediction tools, BioTransformer also supports the prediction of metabolism of small molecules by environmental microbes. The integration of environmental metabolism with endogenous human and gut microbial metabolism allows BioTransformer to address many of the predictive metabolic needs of metabolomics or exposomics researchers, which tend to span a much wider range than, say, drug researchers, food chemists or environmental scientists.
Despite its strengths, BioTransformer is not without some limitations. Addressing these would certainly make the program much more flexible, more accurate, and more comprehensive. Obvious improvements for the current version of BioTransformer include: (1) the validation of BioTransformer's predictions for a larger and more diverse test set of molecules; (2) the experimental validation of BioTransformer's BMIT predictions for a larger set of molecules and experimental data; (3) the expansion of the reaction knowledgebase to cover more reactions, and (4) the addition of new options for metabolite prediction/ranking.
Acknowledgements We would like to thank  Nazanin Assempour  (NA),  Ithayavani Iynkkaran (II) ,  David Arndt  (DA),  Carin Li (CL),  Xuan Cao  (XC),  Zachary Budinski  (ZB),  An ChI Guo (AG) , and  Hasan Bradan  (HB) from the  Wishart lab  for their contributions. NA, and II helped coordinating early efforts in the development of MetXBioDB. DA, XC, ZB contributed in the curation of MetXBioDB. DA, XC, ZB, CL, HB, and AG contributed in improving the design and functionality of the webserver. We would also like to thank  Kathrin Fenner  from the  Swiss Federal Institute of Aquatic Science and Technology (EAWAG)  for answering some of our questions in regard to the  EAWAG-BBD/PPS  system.
Funding This work was supported by grants from  Alberta Innovates  (the  Collaborative Research and Innovation Opportunity Fund) ,  Genome Alberta  (a division of  Genome Canada ), the  Canadian Institutes of Health Research (CIHR) , and the  Agence Nationale de la Recherche  (# ANR-14-HDHL-0002-02 ) for the  FoodBAll  project ( JPI HDHL ). JF was an AgreenSkills + fellow (app. ID 1007).
Availability and requirements Project name: BioTransformer. Project home page: Server http://www. biotr ansfo rmer. ca; Command-line tool/Library https ://bitbu cket. org/djoum bou/ biotr ansfo rmerj ar. Operating system(s): Web service-platform independent. Command-line tool/Library-Windows, Linux, MacOS. Programming language: Java. Other requirements: Java 1. 8. Any restrictions to use by nonacademics: No login requirement for running or accessing the results using the web service. Permission of the authors is required for use in commercial applications. License: GPLv2. 1.