dataesr/scientific-paragraphs-categorization huggingface dataset:
Rchempp Rchempp is a web service that identifies structurally similar compounds (structural analogs) in large compound databases  [195]. The service allows compounds to be queried in the widely used ChEMBL (version 18) and DrugBank (version 4. 0) databases mentioned above and the Connectivity Map database  [196]. Rchemcpp utilizes similarity functions, i. e. molecule kernels, as measures for structural similarity. By exploiting information contained in public databases, the web service facilitates many applications crucial for the drug development process, such as prioritizing compounds after screening or reducing adverse side effects during late phases.
SwissSimilarity This is a web tool for rapid LBVS of small to large libraries of small molecules  [182]. Screenable compounds include drugs, bioactive (databases used: ChEMBL, Chemical Entities of Biological Interest (ChEBI), G protein-coupled receptor (GPCR) ligands, hmdb and ligands from the protein data bank (PDB)) and commercial molecules (databases used: Zinc, Asinex, Aldrich, ChemBridge, ChemDiv, Enamine. ), as well as over 280 million virtual compounds (Sigma Aldrich library) readily synthesizable from commercially available synthetic reagents. Predictions can be carried out on-the-fly using six different screening approaches, including 2D molecular fingerprints (e. g. FP2 fingerprints) as well as 3D similarity methodologies (e. g. Shape-IT, Align-IT). SwissSimilarity was for instance used to find analogs of novel inhibitors of the membrane-associated inhibitory kinase PKMYT1  [197]  or to generate a screening library to find histamine H3 receptor ligands  [198].
ZINCPharmer This is an online server for searching the purchasable compounds of the ZINC database using the Pharmer pharmacophore search technology  [199]. The commercial collection MolPort can also be screened. A pharmacophore describes the spatial arrangement of the essential features of an interaction. Compounds that match a well-defined pharmacophore serve as potential lead compounds for drug discovery. ZINCPharmer provides tools for constructing and refining pharmacophore hypotheses directly from an input molecular structure. A search of 176 million conformers of 18. 3 million compounds is performed. The results can be analyzed interactively, or the aligned structures can be downloaded for offline analysis.
ChemSAR Predictive models based on machine learning techniques have proven to be effective in drug discovery. However, to develop such statistical models, researchers usually have to use multiple tools and the process requires many different steps (e. g. RDKit or ChemoPy package for molecular descriptor calculation, ChemAxon Standardizer for structure preprocessing, scikitlearn package for model building and statistical analysis and ggplot2 package for data visualization, etc. ). Strong programming skills are needed to develop such models. ChemSAR is a web-based pipelining platform for generating SAR classification models (random forest, support vector machine, naive bayes, Knearest neighbors and decision tree) of small molecules  [200]. The capabilities of ChemSAR include the validation and standardization of chemical structure representation, the computation of 783 1D/2D molecular descriptors and 10 types of widely used fingerprints for small molecules, the filtering methods for feature selection, the generation of predictive models via a stepby-step job submission process, model interpretation in terms of feature importance and tree visualization, as well as a helpful report generation system.
DPubChem This is a web tool for deriving QSAR models that implement the state-of-the-art machine learning techniques (classification models with random forest, AdaBoost, support vector machine, naive Bayes, K-nearest neighbors and decision tree) to enhance the precision of the models and enable efficient analyses of experiments from PubChem BioAssay database  [201]. DPubChem has a simple interface that provides various options to users. Users select a PubChem BioAssay, compute chemical features (e. g. various fingerprints and molecular descriptors are available) and correction for class imbalance can be turned on. Once the statistical model is built, users can screen molecules. DPubChem predicted active compounds for 300 datasets with an average geometric mean and F1 score of 76. 68% and 76.
DeepScreen This is a web server with integration of the state-of-art deep learning algorithm, which utilizes either annotated databases such as ChEMBL or user-provided datasets and performs virtual screening to propose chemical probes or drugs for a specific target  [202]. With DeepScreening, users can construct a deep learning model to generate target-focused compound libraries. The constructed classification or regression models can then be subsequently used for virtual screening against chemical vendor collections or other libraries (e. g. Specs, Enamine, ion channels, epigenetics, DrugBank or GPCR libraries) or a de novo compound library can be generated via the de novo module.
Machine learning-based virtual screening tool As discussed above, virtual screening can be used in the early-phase of drug discovery. Because there are thousands of bioactive compounds, it might be possible to partially distinguish drug-like and nondrug-like molecules. Statistical machine learning methods can be used for classification purpose. Machine learning-based virtual screening tool (MLViS) is a tool that attempts to classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based, kernel-based, ensemble and other algorithms  [203]. The application can also create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. Moreover, users can connect the PubChem database to download molecular information and to build 2D structures of the selected compounds.
Online Chemical Modeling Environment The Online Chemical Modeling Environment (OCHEM) is a webbased platform that aims to automate and simplify the typical steps required for QSAR modeling  [204, 205]. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of the records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods (e.
ChemProt-3 The tool compiles multiple chemical-protein annotation resources integrated with diseases and clinical outcomes information. The data sources include ChEMBL  [168] , BindingDB  [207] , DrugBank  [171] , STITCH  [208]. information from the Anatomical Therapeutic Classification System and side effect data from Sider  [209]. Biological data were for instance obtained from KEGG  [210] , Reactome  [211] , GeneCards  [212]. All the compound bioactivity data were stored in the ChemProt database following an internal curation procedure  [213]. Compounds can be compared with the Daylight-like fingerprints as implemented in RDKit and calculation of the Tanimoto coefficient. The Similarity Ensemble Approach  [214]  is also implemented. Naïve Bayes classifier models have also been developed for about 850 proteins (i.
e. for proteins with sufficient compound bioactivity data) and a visual interface that enables navigation of the pharmacological space for the identified small molecules is also available. In the last version of the service, the authors take the example of caffeine as a query compound and show that the molecule is similar to 105 compounds present in the database and could possibly be associated to 449 proteins. The visual heatmap display shows weak-to-strong association with the different proteins identified  [213].
Multifingerprint Similarity Search aLgorithm (MuSSel) MuSSel is a predictive tool to find putative protein drug targets for a query compound. Predictions are automatically made by screening a large collection of 611 333 small molecules having high-quality experimental bioactivity data covering 3357 protein drug targets selected from the ChEMBL database  [218, 219]. Eighteen different fingerprints are calculated, different similarity threshold values were investigated and the notion of activity cliffs was also explored and the approach was then implemented online.
PPB2 The first version of the tool proposes putative protein targets for a query compound  [220]. It searches through a database containing the ligands of 4613 targets extracted from the ChEMBL database. PPB performs target prediction using 10 different fingerprints separately and returns the predefined number (by default 20) of the top predicted targets using each of them. The result is provided as a consolidated table of annotated targets and the similarity values for each selected fingerprints are shown. Links to the ChEMBL compounds and targets are available and results can be downloaded. Another version of PPB is available and is named PPB2. In this version, 1720 targets were extracted from the ChEMBL database. Ligand similarities are computed with three main types of fingerprints and machine learning approaches are also used (e.
SEA The tool takes a query molecule as input and performs predictions for about 4160 targets using annotated chemical data extracted from the ChEMBL database  [214]. A list of predicted targets annotated with P-values and Tanimoto coefficients of the most similar ligand of each of the predicted targets of the query molecule are provided. SEA only returns predicted targets for which P-values are significantly low. SEA uses the concept of raw similarity scores coupled with a statistical model to compare query molecules to another set of ligands. Pairwise extended-connectivity fingerprints (ECFP4) Tanimoto coefficient similarity is initially computed (using the RDKit toolbox). The significance of the score is then further assessed by using a statistical model built on a random distribution of the raw scores.
Weighted deep learning and random forest (WDL-RF) The server uses weighted deep learning and random forest, to model the bioactivity of GPCR-associated ligand molecules  [228]. The protocol consists of two consecutive stages: (i) molecular fingerprint generation through a new weighted deep learning method and (ii) bioactivity calculations with a random forest model. GPCRs data were taken from the UniProt database and from the GPCR-Ligand Association (GLASS) database, which included over 519 000 unique GPCR-ligand interaction entries. Compounds in SMILES format can be uploaded and the user selects the GPCR name/type for activity predictions.
CaverWeb Protein tunnels and channels are key transport pathways that allow ligands to pass between proteins' external and internal environments. CaverWeb (combine Caver and CaverDock implemented online) is a new tool for analyzing the ligand passage through the biomolecules  [231, 232]. The method uses a modified version of the AutoDock Vina docking algorithm for ligand placement docking and implements a parallel heuristic algorithm to search the space of possible trajectories. It can assess many ligands in contrast to many other tools that typically involve the use of molecular dynamics. The tool was found more robust than two related packages SLITHER  [233]  and MoMA-LigPath  [234]  on the evaluated datasets. In its present form, CaverDock cannot robustly address the conformational dynamics of the protein structure.
DOCK blaster The service allows users to dock ligands obtained from the ZINC database or from the Directory of Useful Decoys (DUD) Decoy maker service in the protein of interest using the program DOCK  [235]. The tool was benchmarked and found to give reasonable results and half the time, the known bioactive ligands ranked the can be interactively visualized online Open n/a  [290]  among the top 5% of 100 physically matched decoys chosen on the fly. The tool was for instance used to find opioid analgesics  [236]  or allosteric modulators of the M2 muscarinic acetylcholine receptor  [237].
DockThor This web server is dedicated to protein-ligand docking simulation  [240]. The DockThor program uses a grid-based method that employs a steady-state genetic algorithm as a search engine and the MMFF94S force field as the scoring function for pose evaluation. The webserver provides the major steps of ligand and protein preparation, it is possible to change the protonation states of the target amino acid and to define the degree of flexibility of the ligand. At present, it is possible to perform virtual screening experiments with a maximum of 100 compounds as a guest user or 1000 compounds as a registered user. Several optimizations have been introduced in the tool and the last version, DockThor 2, has been shown very efficient to dock flexible ligands such as peptides  [241].
iScreen This server is dedicated to virtual screening, it uses the traditional Chinese medicine (TCM) database  [249]  for the compound collection and PLANTS  [245]  for docking. The service offers a protein preparation tool that both extracts protein of interest from a raw input file and estimates the size of the ligand bind site. For customized docking, several services are available including standard, in-water, pH environment and flexible docking modes. TCM de novo drug design can also be carried out via iScreen.
Pharmit This tool provides an online, interactive environment for the virtual screening of large compound databases  [193]. As seen above, Pharmit can be used as a ligand-based pharmacophore screening tool, but it can also perform receptor-based pharmacophore screening when the 3D structure of a ligand-protein complex is available. Pharmit uses the Volumetric Aligned Molecular Shapes method to search shapes. The shape queries may be provided by the user or extracted directly from the Protein Data Bank code of the protein-ligand complex.
Sanjeevini The server allows users to dock Lipinski compliant ligands present in several in-built databases or to draw and dock specific ligands  [254]. Sanjeevini performs a series of computational steps such as preparation of the protein and the ligand from the files uploaded, docks the candidate molecule at the binding site via a Monte Carlo algorithm (for protein or nucleic acid targets), minimizes and scores the docked complex (three scoring functions are available, one for protein-ligand complexes, one for Zn containing metalloproteinase and one for nucleic acid targets) in an automated mode. The binding pocket can be specified by the users or predicted.
Dock/undock or rescoring tools Automatic Molecular Mechanics Optimization for in silico Screening (AMMOS2) is a web server for protein-ligand-water complexes refinement via molecular mechanics  [255]. The protocol employs atomic-level energy minimization of experimental ligand-protein complexes or of ligands docked for instance via SBVS computations. The web server is based on the standalone software Automatic Molecular Mechanics Optimization for in silico Screening (AMMOS). AMMOS utilizes the physics-based force field AMMP sp4 and performs optimization of proteinligand interactions at five levels of flexibility of the protein receptor. In AMMOS2, the users can also include explicit water molecules and individual metal ions during the minimization. The 3D structure of the complexes can be visualized and specific protein-ligand interactions can be seen using Protein-Ligand Interaction Profiler (PLIP) computations  [256].
Consensus Reverse Docking System The CRDS docks a compound into 5254 protein X-ray structures collected from the sc-PDB database  [282]. Gold, Vina and LeDock binding scores are reported and the targets are ranked by consensus scoring. The first target seen in the output table has thus the highest consensus score. Another output shows which pathways the resulting top 50 targets are associated with  [283]. The top 10 pathways are displayed on a pie chart.
Endocrine Disruptor MONitoring (EDMON v3) The approach  [284]  takes advantage of a new interface between the virtual screening standalone package PLANTS  [245]  and the web server @TOME  [285]  to screen multiple conformations in parallel. It allows users to systematically deduce a shape restraint and binding site boundaries based on the geometry of the original ligand from each crystal structure in a fully automatic manner. Subsequent postprocessing is performed using various chemoinformatics tools including several scoring functions to predict protein-ligand affinity and select an optimal pose. The approach evaluates the applicability of machine learning on the docking outputs of @TOME and PLANTS and ligand similarity measurements. Users have to supply a ligand that is inversely docked into several nuclear hormone receptors, an important class of targets.
Results can be downloaded as SDF file Open Pharmacophore generator Main input: up to 32 ligands in MOL2 format Main output: tables with computed pharmacophores and aligned molecules with a score Open Database with pharmacophore and Main input: a ligand in 3D PDB format Main molecular shape ligand similarity search output: similar aligned on the query. Results utility can be downloaded as SDF file Open and registration is possible Ligand similarity search utility in ChEMBL, Main input: Compounds in MOL/SDF format. Drugbank and the Connectivity Map Main output: similar compounds identified. databases Open Compound database with 2D and 3D ligand Main input: Compounds in SMILES format similarity search utility (can be drawn). Output: compounds with similarity scores. Open Database with molecular shape ligand Main input: a query molecule in 3D SDF similarity search utility format.
Main output: aligned molecules. Open Database with pharmacophore ligand similarity search utility Machine learning models and LBVS SAR model building using machine learning Main input: compounds in SMILES or SDF, data sets in CSV format Main output: compounds with predicted affinity to the target that fit the statistical model. SDF file can be downloaded Open and users can create their own working space URL http://similaritysearch. gdb. tools/ http://mms. dsfarm. uni pd. it/pepMMsMIMIC/ https://bioinfo3d. cs. tau. ac. il/PharmaGist/ http://pharmit. csb. pitt. e du http://shiny. bioinf. jku. a t/Analoging/ http://www. swisssimila rity. ch/ http://usr. marseille. inse rm. fr/ http://zincpharmer. csb. pitt. edu/ http://chemsar. scbdd. co m/ Tools MXFP pepMMsMIMIC PharmaGist Pharmit Rchempp SwissSimilarity USR-VS ZINCPharmer ChemSAR Main input: a ligand in 3D, a PDB file with a protein and the ligand, a protein-protein PDB file, a pharmacophore generated by another software Main output: aligned compounds (can also be downloaded) Open Continued Downloaded from https://academic.
cz/caverweb/ and channels using Vina code. Users can provide multiple ligands either management as files with coordinates, ZINC database documented accession codes or by drawing the structure in a sketcher Main output: docked compounds can be interactively analyzed and visualized. Also, the results can be generated in a PDF document Open http://blaster. docking. org/ Docking with DOCK 3. 6 Main input: Target protein structure in PDB or n/a MOL2 format; Users have an option to specify up to 50 actives and up to 50 inactives as SMILES or ZINC IDs. Docking of larger databases (e. g. lead-like, fragment-like) is allowed after initial control calculations are completed successfully Main output: results can be visualized online or downloaded Open http://covalent. docking. org/ Covalent docking Main input: supply the protein structure file in Data privacy and data PDB or MOL2 format and provide information management about the binding site and the attachment point.
documented The user has to select the covalent database for docking Main output: docked compounds can be visualized online and downloaded Open https://dockthor. lncc. br/v2/ Virtual screening, score with Main input: The target structure is uploaded in n/a the MMFF94S force field. The PDB format. The ligand file (1 or more ligands) version 2 can also deal with should be supplied in MOL2, PDB or SDF format short peptides Main output: the results (different binding modes) can be interactively visualized online) are downloadable Open It is possible to create an account and register https://easyvs. unifei. edu. br/ Virtual screening with Main input: upload target PDB file or supply PDB n/a AutoDock Vina and rescoring code; upload a compound collection in 3D sdf with NNScore format or select libraries of interest from the prepared molecule databases Main output: molecules docked into the target can be downloaded or visualized online Open Downloaded from https://academic.
Different via Autodock Vina paste or upload a protein 3D PDB file Main situations for guest output: compounds docked into the target. users and registered Predicted structures can be downloaded or users visualized online Open http://pharmit. csb. pitt. edu Database with receptor-based Main input: ligand with its receptor in 3D PDB Collect information pharmacophore search utility format Main output: similar compounds only when registration positioned in the binding pocket. Results can be but data remains downloaded as SDF file Open and registration is secure possible http://www. scfbio-iitd. res. in/sa Virtual screening Major input: upload a (protein) target and a n/a njeevini/sanjeevini. jsp candidate drug in PDB format. The user can also select a relevant chemical library for screening Major output: docked protein-ligand complexes along with their predicted binding affinities are emailed to the user.
unimelb. edu. au/ Predict the binding affinity of Main input: upload a single PDB structure of n/a csm_lig/ a protein-small-molecule the protein-small-molecule complex or a complex based on structural compressed file with multiple structures. signatures The molecule information is provided as a tab-separated file containing HET ID and SMILES string for each uploaded PDB Main output: results can be visualized online or downloaded as a Pymol session file for visualizing the ligand interactions Open http://www. ufip. univ-nantes. Assemble and analyze virtual Main input: The target protein is added by n/a Different situations if fr/tools/docknmine/ and experimental interaction entering the UniProt ID and then selecting users are registered or not data the relevant structure from the enumerated entries; Ligand information is added via PDB ID, PubChem ID, or ChEMBL ID; or multiple ligands can be added by uploading an sdf file Main output: The results from docking can be visualized online and graphs can be downloaded Registration is required before job submission https://dokhlab.
med. psu. edu/ Flexible docking with Main input: upload the protein structure in n/a medusadock/ constraints PDB or MOL2 format or input the PDB ID of the receptor; the ligand file is uploaded in mol2 format or ZINC ID of the ligand can be entered Main output: The results from docking can be visualized online Open http://moma. laas. fr Ligand unbinding Main input: the protein-ligand complex is Different situations if users uploaded in PDB format Main output: results are registered or not are downloadable and the ligand unbinding pathway can be visualized online Open Downloaded from https://academic. oup. com/bib/article/22/2/1790/5809605 by Institut Pasteur user on 28 June 2021 AMMOS2 CompScore CSM-Lig dockNmine MedusaDock MoMA-LigPath.