dataesr/scientific-paragraphs-categorization huggingface dataset:
0 dataset  [3]. The emerging understanding of selective modulation of Gamma Amino Butyric Acid (GABA) receptors allows an increase in the development of clinically efficacious and subtype selective drugs for these targets  [1]. The development of various α-2/3 selective agonists is in process, for the treatment of anxiety disorders  [2, 3] , however, the binding modes of these selective modulators is still unknown. In our study, we propose a unified pipeline of chemo genomic analyses of GABA modulators and established a link between their bioactivity, efficacy and mode of action on α-1 and α-2 receptors. We applied this protocol on a dataset of 5,440 GABA modulators and extracted a subset of compounds with desired structure activity relationship (SAR) and structure efficacy relationship (SER).
P19 Mean Information Content (MIC) algorithm: a new approach for fingerprint hybridization and reduction Dawid Warszycki 1 , Marek Śmieja 2 , Andrzej J. Bojarski Fingerprints, a bit representation of compound chemical structure, have been widely used in cheminformatics for many years. The conversion of chemical structures into the bit strings is based on either structural keys or graph representations. Despite the fact that fingerprints with the highest resolution display high performance in virtual screening campaigns  [1] , the presence of a relatively high number of irrelevant bits introduces noise and makes their application more time-consuming. Here, we present a new method of hybrid reduced fingerprints construction-the MIC algorithm. The methodology was applied for fingerprints implemented in the PaDEL-Descriptor software  [2].
Here, we present two decision tree-based, multi-label QSAR models able to simultaneously predict substrates and non-substrates in a total of 1493 compounds for 4 of the main ABC transporters  [2]  (BCRP1, MDR1, MRP1 and MRP2). Each of these transporters corresponds to a binary class variable, taking the value (class label) "substrate" or "non-substrate", and the simultaneous prediction of the four class variables is performed by a multilabel classification model, unlike the conventional (single-label) classification task where just one class variable is predicted. One of the models-a Classifier Chain -was built to consider transporter (class) interaction, and a Binary Relevance model was used as the baseline model. Training, optimization and testing were performed with separate portions of the dataset.
Feature selection was optimized from 5 different methods where the best method chosen for each transporter. Both models were validated with testing against y-randomization. The applicability domain was characterized using the STD method  [3] , and activity cliffs and chemical space coverage were determined. Both multi-label models yielded 70% accuracy in the test set, but considering label interaction lead to more balanced models. The AD was able to reliably map the predictive accuracies across the dataset, which means that predictive reliability of new queries can be correctly determined. Moreover, various mispredictions coincided with activity cliff areas, which means that they could not have been picked up as outliers by any model using similar molecular descriptors. This multilabel classification approach is an appropriate alternative for addressing the prediction of ABC efflux, especially considering the unspecific nature of substrate recognition by the ABC family members.
PTP1B is a long known validated target for type 2 diabetes and obesity, while inhibition of the closely related TC-PTP leads to intolerable side effects in mice  [1]. To collect protein conformations, molecular dynamics simulations were performed. Since for TC-PTP no crystal structure with required starting conformation was available, a homology model was used instead. The collected molecular dynamics frames were aligned and for each frame the binding site was filled with a grid of points using the software POVME2  [2]. The binding site shape information derived from the grids was translated into a matrix containing binary data for presence or absence of each point inside the binding site of the corresponding frame. Different clustering algorithms were then used to extract the present information on binding site shape differences.
In parallel, conformations of docked ligands are used to calculate 3D molecular descriptors (PaDEL software  [3] ), and further concatenated with SIFt vectors. The obtained hybrid fingerprints are used as an input for the selected machine learning methods (e. g. SVM, RF, J48, Naïve Bayes) implemented in WEKA software  [4]. Evaluation using compounds active toward 5-HT 6 , 5-HT 7 and 5-HT 1B receptors, proved that the methodology can be successfully used for supporting virtual screening protocols.
In our use case, P-gp and BCRP inhibition data was collected and their compound overlap was determined. Different ways of solving multi-label problems were explored and compared: label-powerset, binary relevance and classifiers chain. Label-powerset revealed important molecular features for selective or polyspecific inhibitory activity, while binary relevance and classifiers chain allow for more predictive models. The models reveal important molecular features for specific or polyspecific inhibitory activity such as SlogP, the number of donors and acceptors of H bonds, the number of aromatic atoms and the length of the maximum single bond chain. Spain; 5 Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain Correspondence: Christoph Grebner -christoph. grebner@astrazeneca.
Overall, this will allow for efficient sampling of ligand-protein binding events under direct consideration of experimental data. We have made a comprehensive analysis of all IC50 and EC50 values with existing target gene id from the internal AstraZeneca screening and test database IBIS. The data was analyzed to arrive at statistically well-founded estimates for intra laboratory assay reproducibility. We find that the typical experimental precision is higher than commonly assumed with typical standard deviations between 0. 21 and 0. 26 log units. Interestingly the experimental uncertainty is relatively independent of the test technology target-class and the functional readout (e. g. IC50 or EC50). Further we do not observe strong correlations of variability with common physico-chemical properties like logD or solubility.
Generally, three main aspects of a SF define its goodness, they are "docking power", "ranking power" and "scoring power"  [1]. Reportedly, conventional SFs are able to predict binding modes while mostly failed to predict binding affinities. In literature, SFs are typically classified as force-field-based, empirical, and knowledge-based. A first application of machine learning using Random Forest to predict binding affinities shows an increasing of more than 20% in term of Pearson's correlation coefficient in a generic benchmark set with 195 protein-ligand complexes  [2]. Since then, a new class of SF has been intensively studied, the machine learning-based SF. Using CASF databases  [3] , we evaluate 11 novel conventional SFs (e. g. goldscore, DrugScore) and 10 machine learning-based SFs, we could show that scoring and ranking power of machine learning-based SFs are superior in comparison to conventional SFs.
An additional and key aim of the project is to serve as a platform to support the peer-review of scientific articles, in which the underlying primary data is jointly submitted with the written manuscript. Specifically, our project offers two distinct services: I) a preservation service, in which datasets are digitally preserved, and kept externally inaccessible, and II) a publication service, in which datasets receive a DOI and are accessible, free of costs, through our site  [4, 5]. The implementation of an OAI-PMH standard and an open API, for the harvesting of metadata and (published) datasets, respectively, is under development. RADAR is a DFG-funded (2013-2016) initiative, consisting of an interdisciplinary team formed by the FIZ Karlsruhe, TIB Hannover, KIT/SCC, LMU Munich and the IPB Halle.
In addition to that, six regression techniques (random forest regression (RFR), support vector regression (SVR), sparse partial least squares (SPLS), lasso, elastic net and ridge regression) were also used to estimate class probabilities. The quality of the calibration is assessed with reliability diagrams  [3]. In detail, the mean squared error of prediction (MSE) for predicted and actual class membership probabilities are computed to quantify the degree of improvement. Furthermore, the influence of accuracy, correlation structure of the predictor matrix, and dataset size on the quality of class probability estimation was analysed in several simulation studies as well as with real data sets. From these studies, the following conclusions can be drawn: 1) within the accuracy range of 0. 70 to 0.
Textbooks generally agree on the layout of basic compounds, while most 2D chemical layout software has no notion about these. The consequence is, for example, that one can find in online databases drawings of pyridine (a trivial layout case where a simple polygon ring drawing algorithm is sufficient) with the nitrogen in all six possible positions, while we were unable to find any textbook where it was not on the bottom. The Cactvs toolkit is the layout and rendering engine behind the PubChem database. Since more than a decade, the software has been one of the few engines which have been using advanced layout rules implementing implicit conventions in addition to computing a reasonable 2D graph. This rule set has been significantly expanded, and includes improvements implemented in response to feedback of PubChem users and developers, as well as 3rd party vendors which have licensed the engine.
Due to the absence of a crystal structure for human SIRT4, a homology model was generated using different templates and computational approaches. The template selection identified human SIRT5 as most suitable template. MD simulations were carried out on the SIRT4 models in complex with the cofactor and different substrates using program AMBER 12 to understand the stability and conformational changes of the modeled proteins in holo and apo form. In addition, several MD simulations of available crystal structures of SIRT5 in complex and in apo form have been performed and compared with the results obtained for SIRT4. In addition, shape-based virtual screening for inhibitors and activators have been carried out. validation set respectively. Outcome of the study suggest that proposed models could be used to not only elucidate the intricate mechanism of action of small molecules by identifying novel therapeutic target but also provide an opportunity to reposition existing drugs for new therapeutic application.
SMARTSminer's functionality and its integration into the SMARTSeditor software is shown in different classification scenarios. 2. Huntington's disease (HD) is associated with the expansion of polyglutamine (polyQ) stretch of the huntingtin (htt) protein. Above a threshold of 37 glutamines huntingtin exon 1 starts to aggregate in a nucleation dependent manner. A 17-residue N-terminal fragment of exon 1 (N17) was shown to play a crucial role in modulating aggregation propensity and toxicity of htt exon 1. We used molecular dynamics simulations to show that binding of CLR01 induces structural rearrangements within the N17 region of htt exon 1 monomer that leads to change in aggregation pathway of htt. Sleeping Sickness, Leishmaniasis and Malaria, infections transmitted by protozoan parasites, are responsible for a great number of deaths and disease burden in tropical countries.
New treatments are an urgent need. In this work combined correlative modelling (PLS) linking plant metabolite profiles and antiprotozoal activity with chemosystematic information, and in silico chromatographic retention time (tR) prediction to aid dereplication (the identification of known compounds) and the targeted isolation of known and new natural products with anti-protozoal activity. First, PLS correlative modeling (R 3. 1. 1 and The Unscrambler 9. 2) was performed for UHPLC/ESI-QqTOF MS/MS data of extracts from Brazilian Asteraceae (n=140) and their in vitro activities against the protozoa Trypanosoma brucei rhodesiense, Leishmania donovani, and Plasmodium falciparum to select variables (metabolite data: m/z-tR) that were positively correlated with the activity. Secondly, the MS-data of these variables were related to the entries of an in house database containing the elemental formula and calc.
The initial number of possible structures related to an elemental formula (exact mass error: 20 ppm) queried in the database ranged from 5-70. Chemosystematic filtering reduced the number of possible structures to a range of 4-15. Comparison of tRe and tRp then greatly facilitated the identification of known compounds since only 1-2 possibilities remained. Seven of the compounds were isolated and all shown to be active against the protozoan parasites as suggested by the PLS correlation models. Three of them were new natural products. Overall, the combination of techniques proposed herein may complement MS-based dereplication, and can be successfully applied to streamline the discovery of new compounds with biological activity, such as an anti-protozoal potential, in crude extracts without time-consuming activity-guided fractionation.
iScienceSearch (http://isciencesearch. com/iss) is a free of charge application to search over 100 databases on the Internet, by structure, substructure, similarity, text and synonyms. If you type a name, we generate in the background automatically the chemical structure, the CAS Registry Number, other identifiers like AKOS number, and different names, if possible. We call this extended search. This means if you type Tamiflu, you also get answers that are perfectly correct, and where only the word Oseltamivir appears. Oseltamivir is a synonym for Tamiflu. You cannot search Google by chemical structure, and Sci-Finder is not an Internet search engine. iScienceSearch is the system of choice searching the Internet for chemists and anybody who needs chemical information.
Acknowledgements: Ministry of Education, Youth and Sports (COST action GLISTEN ,  CM1207 ,  LD14133 ) and  Czech Science Foundation  ( 15-17269S ). Acknowledgement: This study was partially supported by the  National Centre of Science from Poland  (grant no. 2014/13/N/ ST6/01832 ). Acknowledgments: The study was partially supported by the  Polish-Norwegian Research Programme  operated by the  National Centre for Research and Development  under the Norwegian Financial Mechanism 2009-2014 in the frame of Project  PLATFORMex  ( Pol-Nor/198887/73/2013 ) and by the National Science Center Grant No  DEC-2012/05/B/N27/03076. Acknowledgements: We acknowledge financial support provided by the  Austrian Science Fund (FWF) , Grant  F03502  and from a  'Back to Research Grant ' funded by the  Faculty of Life Sciences, University of Vienna.
We also acknowledge support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement no. [115191], resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  (FP7/2007-2013) and in-kind contribution of EFPIA companies. Acknowledgement: We gratefully acknowledge the  EU  ( FP7   NMTrypI  project grant agreement No. 603240 ) for financial support and members of  NMTrypI  for providing experimental data.
Although such analyses report that pathway annotation improves the MoA information gained than by target prediction alone, these experiments are conducted on small and inconsistent datasets (approximately 1,500 compounds [1]). In this work, we have combined an in silico target prediction tool, utilising over 9. 5 million and 600 million active and inactive datapoints respectively, with automated pathways annotation from the  NCBI BioSystems database. This MoA protocol has been applied to over 6,800 cytotoxic and 300,000 non-cytotoxic compounds from Astra-Zeneca cell viability screens in order to rationalise the cytotoxic MoA. Post-processing of predictions involved the removal of targets with correlated activity due to overlapping bioactive compound annotations. Many of the enriched targets support known mechanisms of cytotoxicity, with multiple pathways clustering around processes responsible for the fidelity of cell cycle, cellular division and metabolism.
Acknowledgements: Financial support by Russian Foundation for Basic Research (Projects 15-03-09075 and 14-29-04084) and Presi- dent Grant for Government Support of Young Russian Scientists (MК- 1003. 2014. 3) are gratefully acknowledged. Reference 1. Scott DJ et al. Functional Ceramic Materials Database: An Online Resource for Materials Research. J Chem Inf Model. 2008;48:449-55. 1,2 , Vitaly P. Solov'ev 1 , Vladislav S. Pervov 3 1 Frumkin Institute of Physical Chemistry and Electrochemistry RAS, Moscow, 119071, Russia; 2 Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141700, Russia; 3 Kurnakov Institute of General and Inorganic Chemistry, Moscow, 119071, Russia Correspondence: Natalia Kireeva -nkireeva@gmail. com Journal of Cheminformatics 2016, 8(Suppl 1):P5 Our research focuses on the development of chemoinformatics meth- ods and algorithms to design new functional electroceramic materi- als.
Ceramic materials are widely used in electrochemical devices such as oxygen separation membranes, solid oxide fuel cell (SOFC) cathodes, dielectric materials etc. Solid oxide fuel cells are of great interest as economical, clean and efficient power generation devices. Fuel cells have several advantages over conventional power generation techniques. P6A new benchmarking dataset for conformer ensemble generators Nils-Ole Friedrich, Kai Sommer, Matthias Rarey, Johannes Kirchmair University of Hamburg, Center for Bioinformatics, Hamburg, 20146, Germany Correspondence: Johannes Kirchmair -kirchmair@zbh. uni-hamburg. de Journal of Cheminformatics 2016, 8(Suppl 1):P6.
for binding site shape clustering to increase selectivity of PTP1B ligands 1 , Valentina Eigner-Pitto 2 1 Georg Thieme Verlag KG, Stuttgart, 70469, Germany; 2 InfoChem GmbH, München, 81241, Germany Correspondence: Valentina Eigner-Pitto -ve@infochem. de Journal of Cheminformatics 2016, 8(Suppl 1):P21 The pharmaceutical and health industry makes continuous efforts to improve and increase the available variety of pharmaceutical treat- ments. Medicinal chemists start the innovation cycle by systematic searches in all available published information, i. e. scientific journals, chemical databases and internal documentations. A challenge is the existence of two separate information repositories: full text information and chemical structures/reactions contained in databases. This talk will describe how InfoChem has developed a technological infrastructure within the MarkLogic platform that allows effective and integrated searches in both full-text and chemical information.
iScienceSearch (http://isciencesearch. com/iss) is a free of charge application to search over 100 databases on the Internet, by structure, substructure, similarity, text and synonyms. If you type a name, we generate in the background automatically the chemical structure, the CAS Registry Number, other identifiers like AKOS number, and different names, if possible. We call this extended search. This means if you type Tamiflu, you also get answers that are perfectly correct, and where only the word Oseltamivir appears. Oseltamivir is a synonym for Tamiflu. You cannot search Google by chemical structure, and Sci-Finder is not an Internet search engine. iScienceSearch is the system of choice searching the Internet for chemists and anybody who needs chemical information. P43 Chemical Registration and Publishing System (ChemRPS) with Windows client Alexander Kos, Hans-Jürgen Himmler AKos GmbH, Steinen, Germany Correspondence: Alexander Kos Journal of Cheminformatics 2016, 8(Suppl 1):P43 Small chemical companies or suppliers of chemicals you would like to have a chemical information systems.
I. INTRODUCTION Heterogeneous mobile devices or networked objects (from smartphones, laptops and wearable devices to embedded objects) are assumed to have the capability of sharing data  [1] : studies point to the existing of more than 50 billion objects by 2020  [2]. The heterogeneity comes from software's, hardware, and architectural point of view as explained in  [3]. Nowadays, mobile computing is an essential part of human life which makes daily life more convenient and effective regardless of time and place  [4]. However, the mobile devices are facing several challenges over communication networks such as mobility and resources, that is, battery life, storage and bandwidth  [5]. Cloud computing is a third party with large-scale storage servers and data centers used to provide infrastructures, software development and distribution platforms with low costs in the computing technology.
Cloud computing also enables elastically on-demand services to the users. Hence, mobile applications over Cloud computing can be rapidly provisioned and released with minimal efforts of service providers and management  [4]. Therefore, the Mobile Cloud Computing (MCC) paradigm came into picture by combining Cloud computing with mobile environment. MCC provides full Cloud services to its users. This mobile Cloud computing paradigm provides the required support for the creation of cyber-physical systems, which may be used to improve the daily life experience of citizens as well as to bring social and economic benefits. Mobile networks are suffering from "capacity crunch", meaning that network providers are struggling to meet the demand of mobile data services. To provide Quality-of-Service to the user, the network provider must provide a rich set of services such as, increasing the network capacity and energy efficiency based on the user's mobility patterns.
In the context of MCC, energy efficiency must be considered not only at the Cloud side but also at the mobile side. The former is used to solve the existing limitation of the latter by using remote resource provider rather than processing/storing data locally  [6]. In terms of saving energy for mobile devices, the externalization of data and applications is viewed as a good solution. However, this depends on the MCC architecture that is considered. In fact, if the externalization occurs between the mobile device and the Cloud, the application or the data offloading may be costly comparatively to a local mobile computation. By pointing out the mobile computing concerns, especially energy consumption, the contribution of this paper is to discuss in detail the existing mobile Cloud computing architectures to address the issue.
V. EXPERIMENTATION AND RESULTS In order to study the energy efficiency of the mobile device when using Cloudlet architecture, we conducted some experiments by considering different approaches: OSGi, overlay, and container based solutions. To experiment on this work, we have used two devices to implement three discussed Cloudlet based architectures. Performance comparisons is conducted on a LAN (Local Area Network) to overcome Internet WAN latencies that are common to all solutions. • Cloudlet side: Linux Ubuntu 14. 04 LTS <64 bits> (CORE i7) • Mobile device side: Windows 7/Linux-based OS <32 bits> (DUAL CORE) (1) Elijah project based on overlay notion. The application overlay is calculated as the binary diff (VCDIFF RFC3284) using xdelta3 tool  [19] , between the complete VM disk image and the base VM disk image.