dataesr/scientific-paragraphs-categorization huggingface dataset:
Projects targeting several NR Besides articles that were solely dedicated to a specific NR presented above, other projects focused on ensemble of NRs to provide more global NRs ligand identification models. Except one article  (106) , all these projects fall under a toxicological scope with the aim of identifying potential EDCs and understanding their mechanism of action. The most targeted NR combination is ER and AR due to extensive literature and data available. For example, Li et al. (31)  studied the ability of some EDCs to present a dual activity by interacting with both AR and ER. To do so, they constructed ER binding models using a QSAR approach and these models were used to screen a database of AR antagonist compounds. It is to note that some of these projects are made available as webservers and software  (12, (103) (104) (105).
Open VirtualToxLab (  105 ) is a software that estimates the toxicological potential of the screened compounds by relying on an automated combination of flexible docking and free energy calculations. Open VirtualToxLab focuses on 16 biological targets among which are 9 NR: AR, ERa, ERb, GR, LXR, MR, PPARg, PR, TRa, TRb. The proof of concept of this software was established with a series of 2564 compounds yielding accurate predictions [C. f. Table  2  of (  105 )]. Endocrine disruptome (  104 ) is a freely available open-source project that allows users to test the ED potential of their compounds against 14 NR involved in several biological processes: AR, ER a, ER b, GR, PPARa, PPARb, PPARg, PR, RXRa, and TRa and TRb. This webserver is based on docking models elaborated for each NR individually.
The major drawback of this tool is the use of DUD-E decoys to validate the protocol. These decoys are putative inactive compounds, and not experimentally validated inactive compounds, which may bias the evaluation of the performance of the models. ProTox-II (  103 ) is a webserver predicting the potential toxicity of small molecules by using 33 models of different toxicity endpoints. Among these endpoints are AhR, AR, ER and PPARg signaling pathways. The models for each one of these NRs performed with high accuracy on the Tox21 dataset [C. f Table  S2  of (  103 )].
Due to its pivotal role as an environmental pollution sensor and mediator, AhR emerges as a major target for toxic compounds which is illustrated by the predominance of reported toxicological projects (8 toxicological among 10 in total). Most of the data used in these projects are collected from the literature and these studies are in their large majority dedicated to specific chemical series of environmental compounds that have been proved experimentally to be able to interact with AhR  (71, 149, 150). The corresponding models are associated with high predictive power, but their applicability domain is relatively limited. Additionally, data from the Tox21 project was also used with both SB and LB methods. However, the application of SB methods is limited by the fact that the 3D structure of this receptor has not been solved yet.
In contrast, EDCs can present different pharmacological profiles and it is thus important to evaluate both type of structures to ensure that the screening is able to retrieve all EDCs regardless of their pharmacological profile. In the reviewed articles, two main docking approaches have been listed: single and an ensemble structure docking. In the ensemble structure docking approach, a ligand is successively docked against several protein conformations (Multiple PDB entries or snapshots extracted from MD simulations or a gaussian transformation of the atom localization) and the results are post processed to only keep the best score among all the structures. This approach provides a better picture of the protein flexibility but is more computationally and time expensive and it is not always associated to enhanced docking performances  (157).
Because a huge number of predefined descriptors can be generated, it is important to correctly select the ones that will accurately translate the link between the chemical structure and the associated activity  (160). A classical approach is illustrated in the publication of Wang et al. (88)  in which QSAR models dedicated to the identification of agonist and antagonist ligands of TR are presented. In this study, the software Dragon was used to initially generate over 1600 descriptors. This number was then reduced by discarding the descriptors highly correlated and selecting only those directly related to the structure, intuitive and easy to understand. Conversely, DL methods emancipate from descriptors computation and selection. Instead, DL methods ensures automatic feature extractions during the training phase.
Data splitting An important step prior the generation of LB models is to split the available ligands data into train and test set  (160). The stake of a good data split is to obtain a test set that is somehow representative of the training set chemical space. In most of the reviewed article, data was split randomly as it is commonly done to avoid biased evaluation of the model. However, this randomness may lead to an uneven distribution of active and inactive compounds between the train and the test set especially if the active/inactive ratio is initially low. In this sense, Capuzzi et al. (101)  build QSAR models relying on two sets of databases: (1) the native Tox21 set of data with a ratio of active: inactive equal to 1:10 and (2) a balanced set generated down-sampled from the inactive data of the Tox21 native dataset i.
e. for each active compounds an equivalent inactive was selected either randomly or based on the highest Tanimoto score within the inactive pool of compounds. It is to note that using the balanced set leads to a decrease in the accuracy of the model in comparison to when they used the native (unbalanced) dataset. Randomly splitting data may also result in a test set that is not representative of the train especially when the initial data are structurally diverse. Wang, Xing et al. (  88 ) used self-organizing map (an ANN algorithm) to project an ensemble of structurally diverse compounds on a low dimensionality grid. This helped visualizing the space and the selection of a test set representative of the overall dataset and lead to average accuracies of 83. 1-97. 2%. In other studies, the trade-off between randomness and unbiased subdivision was achieved with bootstrapping.
Databases A chemical database (DB) is an organized collection of compounds with relevant information on their chemical structures together with activity data collected from in vivo and/or in vitro experiments and sometimes in silico predicted activities. Several DB dedicated to NR exist and we decided in this review, to focus on the ones available in open access as presented in Table  5. In silico studies focusing on NR are numerous and the choice of the databases to use to construct predictive models depends on the aim of the project. Moreover, and especially for drug design projects, some research teams chose to use in house databases i. e. a collection of compounds issued as a result of experimental work in the laboratory. These databases can be composed of one or several chemical series originating from a single hit or a known drug scaffold or, in some cases focused libraries.
Finally, some projects rely on several existing databases, either to combine the molecules resulting in larger databases for the same purpose (therapeutic or toxicological) or use a database for the training step and another for the external validation. For example, Réau et al. (  29 ) developed a docking and a pharmacophore modeling strategy for identifying AR agonist compounds relying on the NR-DBIND data and used as an external validation set the compounds from the tox21 challenge.
Data balance Along with active compounds, it is also important to construct, optimize and evaluate NRs prediction models to include negative data  (168). Positive data are usually resulting from biological or cellular in vivo assays but, negative data has long been constituted by presumed inactive compounds  (121). Since negative data have a crucial impact in influencing the performance of a model  (168) , recent efforts have been made to include carefully selected and experimentally validated inactive compounds [NR-DBIND  (169) ]. An additional issue is the proportionality between active and inactive compounds in DB. When collecting data from scientific literature, very few inactive compounds are retrieved. Conversely, in databases presenting high throughput screening results, inactive compounds usually outnumbered the active ones.
Through this review, we decided to evaluate this point in the NRs in silico research field. For each reviewed model, the level of reproducibility was graded according to the availability of databases and the model procedure's parameters and/or code. The "high" grade was used to describe models providing both components within the article, the "medium" was associated with articles with partial available data. Finally, the reproducibility was described as "low" for models where no data were available. For most of the reviewed articles' reproducibility was rated "medium" (52 out of 89 articles) and only a low proportion was described to have a "high" reproducibility (16 out of 89). These results focused on the NRs are in concordance with other large-scale studies  (173). For example, in a study of 2011, the analysis of 500 papers published in the top 50 journals across scientific fields showed that only 9% of these papers enclosed full primary data  (171).
This article is a review of the studies dedicated to NR ligands prediction for both therapeutic and toxicological purposes, published in the last decade. 89 articles concerning 14 NR subfamilies were carefully read and analyzed in order to retrieve the most commonly used computational methods to develop predictive models, to retrieve the databases deployed in the model building process. Some issues facing the model building process were addressed like the assays endpoint discrepancies and data balance were also addressed and how authors managed to overcome them. This review emerged from the need to identify most of the in silico initiatives undertaken on NR and can be used as a starting point for future investigations on the subject not only to appreciate the importance of NR in both therapeutic and toxicological fields, but also to learn from previous experiences, encourage the elaboration of more accurate predictions and motivate collaborations.
drugdesign. fr/ 9,905 compounds and 339 structures of the NRLiSt BDB Yes BDB ONRLDB https://academic. oup. com/database/article/ ∼11 000 ligands, of which ∼6500 are unique. Yes doi/10. 1093/database/bav112/2433243 NURA https://www-sciencedirect-com. proxybib-pp. bioactivity data for 15,247 molecules and 11 NRs Yes (ERa andb, PPARGag, cnam. fr/science/article/pii/ and d, AR, GR,PR, FXR, S0041008X20303707?via%3Dihub RXR and PXR).
Literature trends To find relevant publications, we searched publication databases using combinations of Medical Subject Heading (MeSH) keywords, the controlled vocabulary thesaurus used for indexing articles for PubMed. Combinations of keywords, such as 'computer simulation' with 'cardiovascular agents' or 'antineoplastic agents' or 'antiviral agents' yielded 1129, 5502, and 2706 results respectively from 1987 until April 15, 2021. The impact on pharmacology resulting from the development of multiscale models for complex chemical systems is shown in Fig. 1. Indeed, the percentage of publications increased drastically from 2012 onward for each of the three fields, although the slopes of these increases differ. The proportion of publications related to simulations in the field of antineoplastic and antiviral agents increased continuously, whereas that related to simulation in the field of cardiovascular agents (Fig.
In terms of the number of 3D protein structures that have been elucidated so far, and recorded in the Protein Data Bank (PDB), there are fewer for CVD (448) compared with oncology (2768) and virology (2498). 25 Finally, it might be that there are unique limitations to using computer simulations to study cardiovascular agents compared with other fields. Therefore, here we review molecular modeling techniques and address their complementarity and limitations in the cardiovascular field.
However, the tertiary structure of the pharmacological target is sometimes unknown, because only the endogenous ligands have been described,  26  or because the target is a transmembrane protein, which is difficult to extract from its lipid environment and characterize accurately with sufficient resolution. When the tertiary structure of the pharmacological target is known, structure-based methods can be used to further investigate the interaction between the target and drug candidates. If the tertiary structure is unknown, ligand-based methods can be used to identify drugs with homologies with endogenous ligands. In both cases, virtual screening can be used to identify leads, but with different approaches  29. When structure-based methods are used, screening is performed by two major techniques: (i) by direct docking molecules from a database to the structure of the target and by evaluating this binding, with software such as AutoDock  30  , AutoDock Vina 31 , or GOLD 32 ; and (ii) by drug-target interaction AI or regression models pretrained on available target-ligand pairs with known binding affinities or biological activities.
Free software is available, such as GROMACS, 37 NAMD, 38 AMBER, 39 or CHARMM. 40 MD can also be used to investigate the time-dependent dynamical and structural properties of the systems at the atomic level. The effects of the environment can also be considered. For instance, solvent, pH, temperature, and pressure can be modified in accordance with experimental conditions. For example, lipid-protein interactions can affect protein conformation and activity,  41  and MD makes it possible to simulate systems including complex mixtures of lipids, sugars, and cholesterol molecules with transmembrane proteins. MD simulations usually determine energy values and are the primary means for determining ligand-binding free energies. For instance, free energies can be computed through free energy perturbation (FEP) methods, which are based on statistical mechanics and calculate free energy differences in pro-tein-ligand affinities in a more rigorous way.
KEYNOTE (GREEN) Drug Discovery Today Polarization effects can be taken into account with the use of polarizable force fields. 45 In enzymes, when ligands chemically 'react' with the active site of the target, only quantum chemistry-based methods are suitable for studying reactivity at the molecular level. Software, such as Gaussian, 46 NWChem, 47 ORCA,  48  and TeraChem, 49 make it possible to determine binding energies, structural features such as bond lengths and angles, and characteristic vibration energies. Quantum mechanics (QM) methods also make it possible to determine energy barriers along reaction pathways, and through reaction kinetics. Most docking programs are rooted in classical molecular mechanics. 1] For instance, Cavasotto and Aucar developed a new QM scoring function for AutoDock Vina for high-throughput docking.
55 harmacophore: Description of a molecule containing specific physicochemical characteristics (electron donor, electron acceptor, aromatic ring, etc. ). ] Quantum chemistry: numerical simulation technique based on Born-Oppenheimer approximation to resolve a Schrödinger equation that allows access to reactivity between molecules and their transition state, Gibbs free energy of reaction (useful to elucidate a reaction mechanism). 58 irtual screening: uses a computer simulation method to identify ligands from a database that bind to the target. 54 verview of publications reporting on computer simulations used in the cardiovascular field, found using the MeSH database of MedLine with the keywords 'computer simulation' and 'cardiovascular agents'    how MD makes it possible to validate the computer model of the therapeutic target before using it for other computer calculations.
169 Computer modeling can help to predict hERG-mediated cardiotoxic drugs in a unified framework for in silico drug discovery, development, and safety assessment  207. Moreover, prediction of adverse drug reactions is essential in drug development. Different machine learning algorithms for cardiovascular adverse drug reaction prediction have been used, including features such as biological data (transporters, enzymes, and target involved), chemical data (substructure fingerprints) and phenotypic data (other adverse drug reaction data and therapeutic indications). 208 To appreciate the accuracy of these predictive models, their ability to predict already reported adverse drug events of known cardiovascular drugs was assessed. Results of the generated model were in accordance with a freely accessible database of adverse effects.
Additionally, some adverse effects, not reported by this database, were predicted by generated models and were in accordance with literature evidence. FEP calculations were done by Mousaei et al. using the SILCS technique to test a set of 55 blockers on the hERG1 channel. pIC50 predictions from the SILCS approach for this set of blockers were found to be in a good agreement relative to experimental data. These calculations showed that FEP computation using the SILCS technique can be used for the rapid screening of small molecules for their cardiotoxic potential as well as for exploring alternative binding pockets in the hERG1 channel with applications to the rational design of activators. 209 These models showed effective prediction of adverse cardiovascular effects, which could be used during drug development.
2 channel 4MS2 (by X-ray) and Homology Two compounds showed higher affinity for channel compared homology modeling modeling/docking with nifedipine Isoxsuprine eNOS/Cystathionine c-lyase 3NOS and 3COG (by X-ray) Pharmacophore/docking/ eNOS binding sites located both on substrate access channel (CSE) MD and within catalytic site. For CSE, binding site is close to substrate access channel and catalytic site. Isoxsuprine involves various mechanisms and is a potent candidate for polypharmacology 1,3,4-Oxadiazole derivatives Factor Xa 1NFY (by X-ray) Docking/quantum Antithrombotic activity predicted by computer simulation was chemistry superior to that of unfractionated heparin Molecules from SPEC database Factor IXa 4Z0K (by X-ray) Pharmacophore/3D- Six molecules out of 75 671 were screened as potential FIXa www.
drugdiscoverytoday. com 991 Various molecules from Alstonia scholaris Molecules from MolMall database New N-(phenylmethyl)-benzoxazol-2-thiones Salvianolic acids from traditional Chinese medicine Factor Xa/tissue plasminogen activator Liver X receptor Macrophage migration inhibitory factor (MIF) P2Y1/P2Y12 receptors QSAR /docking Docking Docking/MD 1CA7, 1GCZ, 1LJT (by X-ray) QSAR/docking 2BOK, 1A5H and 1RTF (by X-ray) 1UHL, 1UPW (by X-ray) 4XNW, 4PXZ, 4NTJ (by X-ray) Ensemble docking/MD inhibitors Some ligands bind t-PA and Factor Xa more optimally compared with streptokinase Two agonist candidates were identified with interactions with key residues. Both complexes occupied similar spatial position of region of LXR and could be potent treatment for LXR-related CVDs or cancers Preferred binding mode for within MIF tautomerase active site predicted for identified potential inhibitor Salvianolic acid A and C antagonized P2Y1 and P2Y12 receptors, whereas salvianolic acid B antagonized P2Y12 only, with high affinity, explaining their potential antithrombotic mechanism KEYNOTE (GREEN) Compounds with high affinity for AT1 and low affinity for hERG identified to design new effective, safe antihypertensive drugs Xanthon derivatives b1-Adrenoreceptor 4BVN (by X-ray) Docking Common binding orientation of xanthan derivatives in catalytic binding pocket of b1 adrenoreceptor identified.
Methods To complete the review, relevant studies and literature were searched from several scientific databases, including PubMed, Google, Google Scholar, and ScienceDirect. The categories of keywords used for the search included "Natural Products" or "Natural Compounds" or "Skin Disorders" or "Skin Disease" or "Mangiferin" and "skin" or "Lutein" and "skin" or "Resveratrol" and "skin" or "Curcumin" and "skin" or "Embelin" and "skin" or "Naringenin" and "skin" or "Quercetin" and "skin" or "Lycopene" and "skin" or "Gingerol" and "skin" or "Apigenin" and "skin". After screening the literature, ten naturally isolated compounds, which have been investigated widely for the effects against various skin disorders, including in vitro and in vivo models, were included.
Acknowledgments The authors acknowledge  Universiti Kuala Lumpur Royal College of Medicine Perak, Ipoh, Perak, Malaysia  for providing the facilities and services required to complete the study. Figures were created with the support of https:// biorender. comunder a paid subscription. SRB is grateful to  Prof. Jagadeesh Bayry  ( INSERM, Centre de Recherche des Cordeliers ,  Sorbonne Universités ,  Université de Paris, France and IIT Palakkad, Palakkad, Kerala, India)  for providing a postdoctoral position.
Weak protonfluorine interactions are observed for 2a with the tert-butyl carbamate group and 4 with a phenyl group (see ESI †). These results suggest a folded conformation in the solution. Also, what interests us mainly is the effect of fluorine on the aromatic nucleus of triazole. Indeed, both fluorine's physi- cal and electronic impact on triazole is unknown. So, we focused on an in-depth study of 5-F triazole in physical and electronic properties. Thanks to the high-resolution X-ray crystal diffraction (CCDC 2141412, † Fig. 2 ), the solid-state structure, the charge distribution, and the electrostatic potential of molecule 2a have been investigated. The conventional atomic positions and thermal parameters were determined from the multipole refinements using the MOPRO software. 18, 19 The numbering schemes for molecule 2a are given in Fig.
DISCUSSION The incidence of IBD is growing everywhere in the world, representing a global pandemic. To better understand the disease and develop new therapeutic options, several actors involved in the pathogenesis are actively investigated, including the immune system and the gut microbiota. Metabolism emerged as a new player in immune functions and inflammation with the advent of the new field of immunometabolism. 29 However, the role of specific metabolites and their mode of action remain mostly elusive. Here, we deliberately focused on Trp metabolism, which has been recognised as a crucial actor in intestinal homeostasis through the generation of active end-products both by host cells and the gut microbiota. 11 Using targeted quantitative metabolomics on samples from a dense time-course experiment in mice and two large human datasets, we identified XANA and KYNA as candidate anti-inflammatory metabolites.
Acknowledgements Nanostring-The authors thank the  UMR 8199 LIGAN-MP Genomics platform (Lille, France)  which belongs to the ' Federation de Recherche' 3508 Labex EGID (European Genomics Institute for Diabetes ;  ANR-10-LABX-46 ). Histology and Bioanalyser-This work has benefited from the facilities and expertise of  @BRIDGe  ( Université Paris-Saclay ,  INRAE ,  AgroParisTech , GABI, 78350 Jouy-en-Josas, France). Mice-The authors thanks  Tatiana Ledent  and IERP team, for breeding. Contributors CM performed the majority of the experiments. CD,  AM, AAu, AAg, MS, YW, AoL, CG, GDC, MP, AnL, JP, CO, LC , LGB-H and PL contributed and/ or supported us for some experiments. AL and PE performed/contributed to trp metabolites dosages. JK, NB, AB, IN-L, CL, PS and LB provided/analyzed human material/samples.
Funding HS received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Programme  ( ERC-2016-StG-71577 ) and from  Agence Nationale de Recherche  ( ANR-20-CE14-0005-1 ). HS and NR received funding from  Association Francois Aupetit (AFA). ZD received financial support grant from  Czech Science Foundation , No  20-00449S.