dataesr/scientific-paragraphs-categorization huggingface dataset:
The Behavior-Interaction-Priority (BIP) framework -rooted in rigorous semantics -allows the construction of systems that are correct-by-design. BIP has been effectively used for the construction and analysis of large systems such as robot controllers and satellite on-board software. Nevertheless, the specification of BIP models is done in a purely textual manner without any code editor support. To facilitate the specification of BIP models, we present DesignBIP, a web-based, collaborative, version-controlled design studio. To promote model scaling and reusability of BIP models, we use a graphical language for modeling parameterized BIP models with rigorous semantics. We present the various services provided by the design studio, including model editors, code editors, consistency checking mechanisms, code generators, and integration with the JavaBIP tool-set.
Model and Code editors A developer provides the system specification by using the dedicated model and Java code editors of DesignBIP. In particular, the developer must specify 1) component behavior in the form of BIP LTS, 2) component interaction in the form of BIP architecture diagrams and 3) the actions associated with transitions and guards, as well as variable declarations directly in Java. Figures  8  and 9  present the DesignBIP LTS and BIP diagram model editors, as well as the Java code editor. In the code editor, the darker parts represent code that was automatically generated by the input given in the model editors, while the bright code parts represent input given directly in the code editor. In the LTS model editor, enforceable and internal transitions are illustrated with solid arrows, while spontaneous transitions are illustrated with dashed arrows.
Behavior generation plugin: LTS to Java code As explained earlier, a developer graphically specifies the LTS that represents component behavior. De-signBIP generates the Java code that describes the LTS specified by the developer. In particular, Design-BIP generates code in the form of Java annotations that describes the ports, component types, transitions, and guards of each LTS. For instance, Java annotations describing the ports of the Route component type (Figure  9 ) are shown in the right-hand side of Figure  8. Firstly, before the plugin generates the Java code, it checks the correct instantiation of each specified LTS according to the constraints defined in the DesignBIP metamodel, which can be found in the technical report  [23]. For instance, the plugin checks whether each LTS has exactly one initial state.
Related Work Model-driven component-based software engineering and development  [6, 16, 29]  has become an accepted practice for tackling software complexity in large-scale systems. It provides mechanisms to sup-port design at the right level of abstraction, error detection, tool integration, verification and maintenance. Systems are built by composing and reusing small, tested building blocks called components. The Generic Modeling Environment (GME) and its successor WebGME are open source Model Integrated Computing (MIC) tools developed for creating domain specific modeling environments and has been effectively applied to a number of domains  [3, 26, 28, 30]. Close to our approach is the ROSMOD design studio  [19]  that also relies on WebGME for collaborative code development and model editing features.
The basic building blocks of ROSMOD are specified in its metamodel which is described in UML class diagrams  [4]. The code development and compilation process have been integrated in the graphical user interface to keep the framework self-sufficient. ROS-MOD integrates code development, code generation, compilation, run-time monitoring, and execution time plot generation. Nevertheless, in ROSMOD component behavior is defined directly with code and thus connection to verification tools is not supported. A plethora of approaches exists for architecture specification. Patterns  [13, 17]  are commonly used for specifying architectures in practical applications. The specification of architectures is usually done in a graphical way using general purpose graphical tools. Such specifications are easy to produce but their meaning may not be clear since the graphical conventions lack formal semantics and thus are not amenable to formal analysis.
Conclusion We presented DesignBIP 6  , which is a web-based, open source design studio  7  for modeling and generating BIP systems. To define system coordination aspects, we used a parameterized graphical language with formal semantics called architecture diagrams, which we extended with BIP coordination primitives. Designing and reusing models that are based on types and not on instances allowed us to cope with the issues of modeling complexity and size. We have implemented dedicated model/code editors, visualizers, as well as integrated the JavaBIP-engine. Additionally, we studied model transformations and implemented dedicated code generation plugins. We have opted for generating code from high-level graphical structures to avoid tedious and error-prone development of Boolean formulas. Rooting the whole modeling and execution process in rigorous semantics allows the connection to checkers and analysis tools.
Virtual Screening (VS) is designed to prospectively help identifying potential hits, i. e. , compounds capable of interacting with a given target and potentially modulate its activity, out of large compound collections. Among the variety of methodologies, it is crucial to select the protocol that is the most adapted to the query/target system under study and that yields the most reliable output. To this aim, the performance of VS methods is commonly evaluated and compared by computing their ability to retrieve active compounds in benchmarking datasets. The benchmarking datasets contain a subset of known active compounds together with a subset of decoys, i. e. , assumed non-active molecules. The composition of both the active and the decoy compounds subsets is critical to limit the biases in the evaluation of the VS methods.
), or Structure-Based (SBVS) if the structural information of the target is used (like in molecular docking studies). The evaluation of VS methods is crucial prior to large library prospective screening to select the appropriate methodology, and subsequently generate reliable outcome on real-life project. Thus, software and workflows must be thoroughly evaluated retrospectively using benchmarking datasets. Such datasets are composed of known active data together with inactive compounds referred to as "decoys"  (Irwin, 2008). Ideally, both active and inactive compounds should be selected on the basis of experimental data. However, the documentation on inactive data is scarce, and putative inactive compounds are generally used instead. Among the common metrics used to estimate the performance of VS methods we find receiver operating characteristics (ROC) curves, the area under the ROC curve (ROC AUC)  (Triballeau et al.
, 2005) , Enrichment Curves (EC), Enrichment Factors (EF) and predictiveness curves  (Empereur-mot et al. , 2015). While conceptually different, they all share the same objective: assess the ability of a given method to identify active compounds as such, and discriminate them from the decoy compounds. However, since the publication of the first benchmarking database in the early 2000s, the composition in both active and decoy compounds have been pointed out to crucially impact VS methods evaluation; several biases have been shown to incline VS assessment outcomes positively or negatively. The difference between the two chemical spaces defined by the active compounds on the one hand and the decoy compounds on the other hand may lead to artificial overestimation of the enrichment  (Bissantz et al.
, 2000). On the contrary, the possible presence of active compounds in the decoy compounds set may introduce an artificial underestimation of the enrichment  (Verdonk et al. , 2004; Good and Oprea, 2008)  since decoys are usually assumed to be inactive rather than proved to be true inactive compounds (i. e. , confirmed inactive through experimental bioassays). New databases were designed to minimize those biases  (Rohrer and Baumann, 2009; Vogel et al. , 2011; Mysinger et al. , 2012; Ibrahim et al. , 2015a). Finally, many studies pointed out that the VS performance depends on the target and its structural properties (structural flexibility, binding site physicochemical properties, etc. ;  Cummings et al. , 2005). Taking this into consideration, and despite the growing number of protein families represented in databases, decoy datasets generation tools were made publicly available in order to allow any scientist to fine-tune target-dependant and reliable benchmarking datasets  (Mysinger et al.
, 2012; Ibrahim et al. , 2015a). In this review, we first present how the notion of decoy compounds evolved from randomly selected putative inactive compounds to rationally selected putative inactive compounds and finally true negative compounds. We develop the successive benchmarking datasets that were published in the literature and their basic to highly refined decoys selection workflows together with the resulting positive or negative biases due to their design. We then detail 5 benchmarking databases or decoy sets generator tools along with their detailed decoy compounds selection that represent the current state-of-the-art as of 2017: their respective composition tend to minimize such biases. Finally, we propose recommendations to select minimally biased benchmarking datasets containing putative inactive compounds as decoy compounds and introduce guidelines to design true inactive compounds containing databases.
Randomly Selected Decoys The first use of a benchmarking database to evaluate virtual screening tools dates back to 2000, with the pioneering work of  Bissantz et al. (2000). The objective of their study was to evaluate the ligands enrichment, i. e. , the ability of docking programs to associate active compounds with the best scores within a compound collection. Three docking programs [Dock  (Kuntz et al. , 1982) , FlexX  (Rarey et al. , 1996) , Gold  (Jones et al. , 1997) ] combined with 7 scoring functions [ChemScore  (Eldridge et al. , 1997 ), Dock, FlexX, Fresno (Rognan et al. , 1999) , Gld, Pmf  (Muegge and Martin, 1999) , Score  (Wang et al. , 1998) ] were evaluated on two different target proteins: Thymidine Kinase (TK) and the ligand binding domain of the Estrogen Receptor α subtype (ER α).
For each target, a dataset containing 10 known ligands and 990 molecules assumed to be inactive (decoy compounds) was created. The decoy compounds were selected following a two-step scheme: (1) the Advanced Chemical Directory (ACD v. 2000-1, Molecular Design Limited, San Leandro) was filtered to eliminate undesired compounds (chemical reagents, inorganic compounds and molecules with unsuitable molecular weights), (2) 990 molecules were randomly selected out of the filtered dataset. The datasets were used to evaluate and compare several docking and scoring schemes. The authors eventually recommended a calibration of docking/consensus scoring schemes on reduced data sets prior to large dataset screens. Later on,  Bissantz et al. (2003)  applied the same protocol to three human GPCRs to investigate whether their homology models were suitable for virtual screening experiments.
A growing interest for virtual screening benchmarking databases soon emerged from the community  (Kellenberger et al. , 2004; Brozell et al. , 2012; Neves et al. , 2012; Repasky et al. , 2012; Spitzer and Jain, 2012). New databases were designed with an increasing complexity in the decoys selection methodologies (see section Benchmarking Databases). Nowadays, benchmarking databases are widely used to evaluate various VS tools  (Kellenberger et al. , 2004; Warren et al. , 2006; McGaughey et al. , 2007; von Korff et al. , 2009; Braga and Andrade, 2013; Ibrahim et al. , 2015a; Pei et al. , 2015)  and to support the identification of hit/lead compounds using LBVS and SBVS  (Allen et al. , 2015; Ruggeri et al. , 2015).
Integration of Physicochemical Filters to the Decoy Compounds Selection In the early 2000s, Diller's group incorporated filters in the decoys selection to ensure that the discrimination they observed was not solely based on the size of the decoy compounds  (Diller and Li, 2003). In addition to the 1,000 kinases inhibitors they retrieved from the literature for 6 kinases (EGFr, VEGFr1, PDGFrβ, FGFr1, SRC, and p38), 32,000 compounds were randomly selected from a filtered version of the MDL Drug Data Report (MDDR). The filters were designed to select decoy compounds displaying similar polarity and molecular weight. Similarly, in 2003, a benchmarking database derived from the MDDR was constructed by  McGovern et al. (McGovern and Shoichet, 2003). Compounds with unwanted functional groups were removed, leading to 95,000 compounds.
The targets of the MDDR for which at least 20 known ligands were available constituted a target dataset (CA II, MMP-3, NEP, PDF, and XO). The remaining compounds were used as decoy compounds. The addition of rational filters was a considerable step forward in the improvement of decoys selection, but due to the commercial licensing of the MDDR, its use was limited (http://www. akosgmbh. de/accelrys/databases/ mddr. htm 1 ). The first benchmarking databases were composed as follows: (1) true active compounds consisted in known ligands extracted from the literature while (2) decoy compounds consisted in putative inactive compounds randomly selected from large databases possibly filtered to be compliant to specific criteria (drug likeness, molecular weight, topological polar surface area. ). Since the decoy compounds were pseudo-randomly selected, they were assumed to be inactive on the defined targets.
Despite the use of the MDDR and the filtering of the decoy compounds, these benchmarking databases displayed a major drawback: the significant differences occurring between the physicochemical properties of the active compounds and decoy compounds led to obvious discrimination and then artificially good enrichments  (Verdonk et al. , 2004; Huang et al. , 2006). In 2006,  Irwin et al. proposed that the decoy compounds should be similar to the known ligands regarding their physicochemical properties to reduce the introduction of bias while being structurally dissimilar to the known ligands to reduce their probability to be active on the defined target. Following these recommendations, they created the DUD database  (Huang et al. , 2006)  that was immediately considered as the gold standard for the evaluation of VS methods.
The DUD database is composed of 2,950 ligands and 95,326 decoys for a total of 40 proteins from 6 classes (nuclear hormone receptors, kinases, serine proteases, metalloenzymes, folate enzymes and others). The decoy compounds were extracted from the drug-like subset of the ZINC database  (Irwin and Shoichet, 2005). The 2D-similarity between known ligands and decoy compounds was computed by calculating the Tanimoto distance based on the CACTVS type 2 substructure keys and 5 physicochemical properties. For each active compound, the 36 molecules sharing the most similar properties while being topologically dissimilar (Tanimoto < 0. 9) were conserved. The evaluation of the performance of DOCK  (Meng et al. , 1992; Wei et al. , 2002; Lorber and Shoichet, 2005; Huang et al. , 2006)  confirmed that uncorrected databases such as the MDDR led to over-optimistic enrichments compared to corrected databases such as the DUD.
Benchmarking Database Biases Despite the precautions taken to build the DUD database, several remaining biases have been reported in the literature. The "analogous bias"  (Good and Oprea, 2008)  lies in the limited chemical space of active compounds that is restricted to the chemical series that have been explored and referenced in databases. The discrimination of the active compounds from decoy compounds can be simplified since the decoy sets would display a larger structural variability that could induce an overestimation of the performance of VS methods. The lack of diversity in the structures of known active compounds limits the training and evaluation of LBVS methods to perform scaffold-hopping, i. e. , the identification of active hit compounds 1 MDDR licensed by Molecular Design, Ltd.
Another widely used method is to fine-tune the active compounds dataset prior to screen to ensure an intrinsic structural diversity. To this aim, the MUV datasets  (Rohrer and Baumann, 2009)  were designed using the Kennard Jones algorithm to obtain an optimal spread of the active compounds in the decoy compounds chemical space while ensuring a balance between the active compounds self-similarity and separation from the decoy compounds. Despite these observations, the most used strategy in the literature still consists in clustering ligands based on 2D descriptors and retain only cluster representatives in the final dataset  (Good and Oprea, 2008; Mysinger et al. , 2012; Bauer et al. , 2013). To reduce artificial enrichment, efforts were made to match as much as possible the physicochemical properties of the decoys to the physicochemical properties of the active compounds.
To this aim, the Maximum Unbiased Validation database (MUV)  (Rohrer and Baumann, 2009)  was designed to ensure embedding of active compounds in the decoy compounds chemical space based on an embedding confidence distance cutoff calibrated on multiple drug-like compounds banks' chemical space. Active compounds that were poorly embedded in the decoy set were discarded. A way to ensure the availability of potential decoy compounds for any ligand is to generate decoys that ignore synthetic feasibility  (Wallach and Lilien, 2011). Other databases select decoys that match active compounds in a multiple physicochemical properties space. The DEKOIS 2. 0  (Ibrahim et al. , 2015a)  proposed a workflow that used 8 physicochemical properties while the DUD-E added net charge to the 5 physicochemical properties already considered in the original DUD.
To address the risk of including false negatives in the decoy set, a common strategy is to select decoy compounds topologically different to any active compound. For this purpose, Bauer et al. introduced the LADS score to guide decoys selection  (Vogel et al. , 2011). In the DUD-E, potential false decoys are avoided by applying a stringent FCFP_6 fingerprints Tanimoto-based filter. It is important to note that since the evaluation of LBVS methods requires that decoy compounds should not be discriminated using basic 2D-based similarity tools, the use of 2D-based dissimilarity filters to avoid false negatives in the decoy set makes the concerned databases inappropriate for the evaluation of the performance of LBVS methods. Therefore, Xia et al. developed a method to select adequate decoys for both SBVS and LBVS  (Xia et al.
, 2014)  by favoring physicochemical similarity as well as topological similarity between active compounds and decoy compounds that passed a primary topological dissimilarity filter. With these improvements, the notion of decoys remained the same-putative inactive compounds-but their selection critically evolved. Ever since, the main progress achieved in the literature lies in the diversification of the protein targets represented in benchmarking databases. The growing need for datasets dedicated to a given target led to (1) an increasing diversity of targets in benchmarking databases [the DUD-E  (Mysinger et al. , 2012)  contains datasets against 102 targets while the previous DUD  (Huang et al. , 2006)  contained datasets only for 40 targets] and (2) highly specialized benchmarking databases focused on a particular class of targets.
Such specialized datasets exist for GPCRs [GPCR ligand library (GLL)/Decoy Database (GDD)  (Gatica and Cavasotto, 2012) ], histone deacetylases [maximal unbiased benchmarking data sets for HDACs-MUBD-HDACs  (Xia et al. , 2015) ], or nuclear receptors [NRLiSt BDB  (Lagarde et al. , 2014a) ]. As a notice, DUD-E or DecoyFinder  (Cereto-Massagué et al. , 2012)  offer automated decoy set generation tools based on the properties of active compounds, enabling the community to easily design and tune their own dataset for a particular target.
, 2014) , Drugbank  (Wishart et al. , 2008 ) that provides annotations for approved drugs; PDBBind  (Wang et al. , 2004 (Wang et al. , , 2005)) , Binding MOAD  (Benson et al. , 2008)  and AffinDB  (Block et al. , 2006 ) that contain binding affinity data for protein-ligand complexes available in the Protein Data Bank (PDB)  (Berman et al. , 2000) ; PDSP Ki database  (Roth et al. , 2000)  that stores screening data from the National Institute of Mental Health's Psychoactive Drug Screening Program; BRENDA  (Placzek et al. , 2017 ) that provides binding constants for enzymes; IUPHAR  (Southan et al. , 2016)  that contains binding information for receptors and ion channels; GLIDA  (Okuno et al. , 2006)  and GPCRDB  (Munk et al. , 2016)  that contains binding data for G-proteincoupled receptors; D3R datasets (Drug Design Data Resource 2  ) that have been provided by pharmaceutical companies and academia and contain affinity data for 7 proteins together with inactive compounds; ToxCast TM /Tox21  (Kavlock et al.
, 2012)  and PCBioAssay  (Wang et al. , 2017)  that provide HTS data for various targets. As an example, the DUD-Enhanced  (Mysinger et al. , 2012 ) (DUD-E) integrates some experimentally validated inactive compounds extracted from ChEMBL in the decoy set in addition to putative inactive compounds: an arbitrary 1 µM cutoff is used to classify ligands in the active set while molecules with no measurable activity at 30 µM or higher concentration were classified into the decoy set. Similarly, the Maximum Unbiased Validation (MUV)  (Rohrer and Baumann, 2009)  datasets are composed of both active and inactive compounds collected from the PubChem BioAssay annotated database. Unwanted compounds, i. e. , compounds that display unwanted activity or binding, can also be used as negatives. For instance, a recent study used ligands of the NRLiSt BDB  (Lagarde et al.
SELECTED DATABASES Maximum Unbiased Validation (MUV) The MUV was designed to propose unbiased datasets in regard to both artificial enrichment and analogous bias by proposing a new approach gleaned from spatial statistics  (Rohrer and Baumann, 2009). The authors ensured homogeneity in activesactives similarity and actives-decoys dispersion in order to reach a random-like distribution of active compounds and decoy compounds in a physicochemical descriptors chemical space. This implies that the molecular properties contained no information about the bioactivities of active and decoy compounds. Datasets were designed for 18 targets with a total of 30 actives and 15,000 decoys for each target.
Initial Compounds Database Potential active and decoy compounds were extracted from HTS experiments available in PCBioAssay (June 2008) (PubChem BioAssay 3 ). In these assays, a primary screen was performed in a large number of compounds (>50,000) and was followed by a low throughput confirmatory screen. Compounds with an experimental EC50 in the confirmatory screen were selected as potential active compounds while inactive compounds from the primary screen were selected as potential decoys.