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  1. Despite an abundance of online databases providing access to chemical data, there is increasing demand for high-quality, structure-curated, open data to meet the various needs of the environmental sciences and co...

    Authors: Antony J. Williams, Christopher M. Grulke, Jeff Edwards, Andrew D. McEachran, Kamel Mansouri, Nancy C. Baker, Grace Patlewicz, Imran Shah, John F. Wambaugh, Richard S. Judson and Ann M. Richard

    Citation: Journal of Cheminformatics 2017 9:61

    Content type: Database

    Published on:

  2. In ligand-based virtual screening experiments, a known active ligand is used in similarity searches to find putative active compounds for the same protein target. When there are several known active molecules,...

    Authors: Francois Berenger, Oanh Vu and Jens Meiler

    Citation: Journal of Cheminformatics 2017 9:60

    Content type: Research Article

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  3. Computational methods to predict binding affinities of small ligands toward relevant biological (off-)targets are helpful in prioritizing the screening and synthesis of new drug candidates, thereby speeding up...

    Authors: Luigi Capoferri, Marc van Dijk, Ariën S. Rustenburg, Tsjerk A. Wassenaar, Derk P. Kooi, Eko A. Rifai, Nico P. E. Vermeulen and Daan P. Geerke

    Citation: Journal of Cheminformatics 2017 9:58

    Content type: Software

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  4. The identification of chemical structures in natural product mixtures is an important task in drug discovery but is still a challenging problem, as structural elucidation is a time-consuming process and is li...

    Authors: Bo-Han Su, Meng-Yu Shen, Yeu-Chern Harn, San-Yuan Wang, Alioune Schurz, Chieh Lin, Olivia A. Lin and Yufeng J. Tseng

    Citation: Journal of Cheminformatics 2017 9:57

    Content type: Research article

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  5. In order to make results of computational scientific research findable, accessible, interoperable and re-usable, it is necessary to decorate them with standardised metadata. However, there are a number of tech...

    Authors: Andrius Merkys, Nicolas Mounet, Andrea Cepellotti, Nicola Marzari, Saulius Gražulis and Giovanni Pizzi

    Citation: Journal of Cheminformatics 2017 9:56

    Content type: Research article

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  6. The development of an electronic lab notebook (ELN) for researchers working in the field of chemical sciences is presented. The web based application is available as an Open Source software that offers modern ...

    Authors: Pierre Tremouilhac, An Nguyen, Yu-Chieh Huang, Serhii Kotov, Dominic Sebastian Lütjohann, Florian Hübsch, Nicole Jung and Stefan Bräse

    Citation: Journal of Cheminformatics 2017 9:54

    Content type: Software

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  7. Authors: Egon L. Willighagen, John W. Mayfield, Jonathan Alvarsson, Arvid Berg, Lars Carlsson, Nina Jeliazkova, Stefan Kuhn, Tomáš Pluskal, Miquel Rojas-Chertó, Ola Spjuth, Gilleain Torrance, Chris T. Evelo, Rajarshi Guha and Christoph Steinbeck

    Citation: Journal of Cheminformatics 2017 9:53

    Content type: Erratum

    Published on:

    The original article was published in Journal of Cheminformatics 2017 9:33

  8. Third-party packages have transformed the Python programming language into a powerful computational-biology tool. Package installation is easy for experienced users, but novices sometimes struggle with depende...

    Authors: Patrick Ropp, Aaron Friedman and Jacob D. Durrant

    Citation: Journal of Cheminformatics 2017 9:52

    Content type: Software

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  9. Recent developments in metabolic engineering have led to the successful biosynthesis of valuable products, such as the precursor of the antimalarial compound, artemisinin, and opioid precursor, thebaine. Synth...

    Authors: Sarah M. Kim, Matthew I. Peña, Mark Moll, George N. Bennett and Lydia E. Kavraki

    Citation: Journal of Cheminformatics 2017 9:51

    Content type: Review

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  10. GPU acceleration is useful in solving complex chemical information problems. Identifying unknown structures from the mass spectra of natural product mixtures has been a desirable yet unresolved issue in metabo...

    Authors: Alioune Schurz, Bo-Han Su, Yi-Shu Tu, Tony Tsung-Yu Lu, Olivia A. Lin and Yufeng J. Tseng

    Citation: Journal of Cheminformatics 2017 9:50

    Content type: Research Article

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  11. On the one hand, ligand efficiency (LE) and the binding efficiency index (BEI), which are binding properties (B) averaged versus the heavy atom count (HAC: LE) or molecular weight (MW: BEI), have recently been...

    Authors: Jaroslaw Polanski, Aleksandra Tkocz and Urszula Kucia

    Citation: Journal of Cheminformatics 2017 9:49

    Content type: Preliminary communication

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  12. This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable...

    Authors: Marcus Olivecrona, Thomas Blaschke, Ola Engkvist and Hongming Chen

    Citation: Journal of Cheminformatics 2017 9:48

    Content type: Research article

    Published on:

  13. Self-interactions Proteins (SIPs) is important for their biological activity owing to the inherent interaction amongst their secondary structures or domains. However, due to the limitations of experimental Sel...

    Authors: Ji-Yong An, Lei Zhang, Yong Zhou, Yu-Jun Zhao and Da-Fu Wang

    Citation: Journal of Cheminformatics 2017 9:47

    Content type: Research article

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  14. Natural products represent a prominent source of pharmaceutically and industrially important agents. Calculating the chemical similarity of two molecules is a central task in cheminformatics, with applications...

    Authors: Michael A. Skinnider, Chris A. Dejong, Brian C. Franczak, Paul D. McNicholas and Nathan A. Magarvey

    Citation: Journal of Cheminformatics 2017 9:46

    Content type: Research article

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  15. The increase of publicly available bioactivity data in recent years has fueled and catalyzed research in chemogenomics, data mining, and modeling approaches. As a direct result, over the past few years a multi...

    Authors: Eelke B. Lenselink, Niels ten Dijke, Brandon Bongers, George Papadatos, Herman W. T. van Vlijmen, Wojtek Kowalczyk, Adriaan P. IJzerman and Gerard J. P. van Westen

    Citation: Journal of Cheminformatics 2017 9:45

    Content type: Research article

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  16. The goal of defining an applicability domain for a predictive classification model is to identify the region in chemical space where the model’s predictions are reliable. The boundary of the applicability doma...

    Authors: Waldemar Klingspohn, Miriam Mathea, Antonius ter Laak, Nikolaus Heinrich and Knut Baumann

    Citation: Journal of Cheminformatics 2017 9:44

    Content type: Research article

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  17. Drug design of protein kinase inhibitors is now greatly enabled by thousands of publicly available X-ray structures, extensive ligand binding data, and optimized scaffolds coming off patent. The extensive dat...

    Authors: Dilip Narayanan, Osman A. B. S. M. Gani, Franz X. E. Gruber and Richard A. Engh

    Citation: Journal of Cheminformatics 2017 9:43

    Content type: Research article

    Published on:

  18. In recent years, research in artificial neural networks has resurged, now under the deep-learning umbrella, and grown extremely popular. Recently reported success of DL techniques in crowd-sourced QSAR and pre...

    Authors: Alexios Koutsoukas, Keith J. Monaghan, Xiaoli Li and Jun Huan

    Citation: Journal of Cheminformatics 2017 9:42

    Content type: Research article

    Published on:

  19. Authors: Jiangming Sun, Nina Jeliazkova, Vladimir Chupakhin, Jose-Felipe Golib-Dzib, Ola Engkvist, Lars Carlsson, Jörg Wegner, Hugo Ceulemans, Ivan Georgiev, Vedrin Jeliazkov, Nikolay Kochev, Thomas J. Ashby and Hongming Chen

    Citation: Journal of Cheminformatics 2017 9:41

    Content type: Erratum

    Published on:

    The original article was published in Journal of Cheminformatics 2017 9:17

  20. The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product ...

    Authors: German A. Preciat Gonzalez, Lemmer R. P. El Assal, Alberto Noronha, Ines Thiele, Hulda S. Haraldsdóttir and Ronan M. T. Fleming

    Citation: Journal of Cheminformatics 2017 9:39

    Content type: Research article

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  21. Analyzing files containing chemical information is at the core of cheminformatics. Each analysis may require a unique workflow. This paper describes the chemalot and chemalot_knime open source packages. Chemal...

    Authors: Man-Ling Lee, Ignacio Aliagas, Jianwen A. Feng, Thomas Gabriel, T. J. O’Donnell, Benjamin D. Sellers, Bernd Wiswedel and Alberto Gobbi

    Citation: Journal of Cheminformatics 2017 9:38

    Content type: Software

    Published on:

  22. In previous reports, Marrero-Ponce et al. proposed algebraic formalisms for characterizing topological (2D) and chiral (2.5D) molecular features through atom- and bond-based ToMoCoMD-CARDD (acronym for Topolo...

    Authors: José R. Valdés-Martiní, Yovani Marrero-Ponce, César R. García-Jacas, Karina Martinez-Mayorga, Stephen J. Barigye, Yasser Silveira Vaz d‘Almeida, Hai Pham-The, Facundo Pérez-Giménez and Carlos A. Morell

    Citation: Journal of Cheminformatics 2017 9:35

    Content type: Software

    Published on:

  23. An important aspect of chemoinformatics and material-informatics is the usage of machine learning algorithms to build Quantitative Structure Activity Relationship (QSAR) models. The RANdom SAmple Consensus (RA...

    Authors: Omer Kaspi, Abraham Yosipof and Hanoch Senderowitz

    Citation: Journal of Cheminformatics 2017 9:34

    Content type: Research article

    Published on:

  24. The Chemistry Development Kit (CDK) is a widely used open source cheminformatics toolkit, providing data structures to represent chemical concepts along with methods to manipulate such structures and perform ...

    Authors: Egon L. Willighagen, John W. Mayfield, Jonathan Alvarsson, Arvid Berg, Lars Carlsson, Nina Jeliazkova, Stefan Kuhn, Tomáš Pluskal, Miquel Rojas-Chertó, Ola Spjuth, Gilleain Torrance, Chris T. Evelo, Rajarshi Guha and Christoph Steinbeck

    Citation: Journal of Cheminformatics 2017 9:33

    Content type: Software

    Published on:

    The Erratum to this article has been published in Journal of Cheminformatics 2017 9:53

  25. In mass spectrometry-based untargeted metabolomics, rarely more than 30% of the compounds are identified. Without the true identity of these molecules it is impossible to draw conclusions about the biological ...

    Authors: Ivana Blaženović, Tobias Kind, Hrvoje Torbašinović, Slobodan Obrenović, Sajjan S. Mehta, Hiroshi Tsugawa, Tobias Wermuth, Nicolas Schauer, Martina Jahn, Rebekka Biedendieck, Dieter Jahn and Oliver Fiehn

    Citation: Journal of Cheminformatics 2017 9:32

    Content type: Research article

    Published on:

  26. Despite the increasingly digital nature of society there are some areas of research that remain firmly rooted in the past; in this case the laboratory notebook, the last remaining paper component of an experim...

    Authors: Samantha Kanza, Cerys Willoughby, Nicholas Gibbins, Richard Whitby, Jeremy Graham Frey, Jana Erjavec, Klemen Zupančič, Matjaž Hren and Katarina Kovač

    Citation: Journal of Cheminformatics 2017 9:31

    Content type: Research article

    Published on:

  27. CPANNatNIC is software for development of counter-propagation artificial neural network models. Besides the interface for training of a new neural network it also provides an interface for visualisation of the...

    Authors: Viktor Drgan, Špela Župerl, Marjan Vračko, Claudia Ileana Cappelli and Marjana Novič

    Citation: Journal of Cheminformatics 2017 9:30

    Content type: Software

    Published on:

  28. The era of big data is influencing the way how rational drug discovery and the development of bioactive molecules is performed and versatile tools are needed to assist in molecular design workflows. Scaffold H...

    Authors: Till Schäfer, Nils Kriege, Lina Humbeck, Karsten Klein, Oliver Koch and Petra Mutzel

    Citation: Journal of Cheminformatics 2017 9:28

    Content type: Software

    Published on:

  29. In recent years, predictive models based on machine learning techniques have proven to be feasible and effective in drug discovery. However, to develop such a model, researchers usually have to combine multipl...

    Authors: Jie Dong, Zhi-Jiang Yao, Min-Feng Zhu, Ning-Ning Wang, Ben Lu, Alex F. Chen, Ai-Ping Lu, Hongyu Miao, Wen-Bin Zeng and Dong-Sheng Cao

    Citation: Journal of Cheminformatics 2017 9:27

    Content type: Software

    Published on:

  30. Patents are an important source of information for effective decision making in drug discovery. Encouragingly, freely accessible patent-chemistry databases are now in the public domain. However, at present the...

    Authors: Stefan Senger

    Citation: Journal of Cheminformatics 2017 9:26

    Content type: Research article

    Published on:

  31. Large purchasable screening libraries of small molecules afforded by commercial vendors are indispensable sources for virtual screening (VS). Selecting an optimal screening library for a specific VS campaign i...

    Authors: Jun Shang, Huiyong Sun, Hui Liu, Fu Chen, Sheng Tian, Peichen Pan, Dan Li, Dexin Kong and Tingjun Hou

    Citation: Journal of Cheminformatics 2017 9:25

    Content type: Research article

    Published on:

  32. Computational prediction of the interaction between drugs and targets is a standing challenge in the field of drug discovery. A number of rather accurate predictions were reported for various binary drug–targe...

    Authors: Tong He, Marten Heidemeyer, Fuqiang Ban, Artem Cherkasov and Martin Ester

    Citation: Journal of Cheminformatics 2017 9:24

    Content type: Research article

    Published on:

  33. The algorithmic, large-scale use and analysis of reaction databases such as Reaxys is currently hindered by the absence of widely adopted standards for publishing reaction data in machine readable formats. Cru...

    Authors: Philipp-Maximilian Jacob, Tian Lan, Jonathan M. Goodman and Alexei A. Lapkin

    Citation: Journal of Cheminformatics 2017 9:23

    Content type: Research article

    Published on:

  34. The fourth round of the Critical Assessment of Small Molecule Identification (CASMI) Contest (www.​casmi-contest.​org) was held in 2016, with two new cat...

    Authors: Emma L. Schymanski, Christoph Ruttkies, Martin Krauss, Céline Brouard, Tobias Kind, Kai Dührkop, Felicity Allen, Arpana Vaniya, Dries Verdegem, Sebastian Böcker, Juho Rousu, Huibin Shen, Hiroshi Tsugawa, Tanvir Sajed, Oliver Fiehn, Bart Ghesquière…

    Citation: Journal of Cheminformatics 2017 9:22

    Content type: Research article

    Published on:

  35. The accuracy of any 3D-QSAR, Pharmacophore and 3D-similarity based chemometric target fishing models are highly dependent on a reasonable sample of active conformations. Since a number of diverse conformationa...

    Authors: Hyoungrae Kim, Cheongyun Jang, Dharmendra K. Yadav and Mi-hyun Kim

    Citation: Journal of Cheminformatics 2017 9:21

    Content type: Methodology

    Published on:

  36. Liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC–ESI–MS/MS) is used for comprehensive metabolome and lipidome analyses. Compound identification relies on similarity match...

    Authors: Hiroshi Tsugawa, Kazutaka Ikeda, Wataru Tanaka, Yuya Senoo, Makoto Arita and Masanori Arita

    Citation: Journal of Cheminformatics 2017 9:19

    Content type: Research article

    Published on:

  37. In previous work, we have assessed the structural similarities between marketed drugs (‘drugs’) and endogenous natural human metabolites (‘metabolites’ or ‘endogenites’), using ‘fingerprint’ methods in common...

    Authors: Steve O’Hagan and Douglas B. Kell

    Citation: Journal of Cheminformatics 2017 9:18

    Content type: Research article

    Published on:

  38. Chemogenomics data generally refers to the activity data of chemical compounds on an array of protein targets and represents an important source of information for building in silico target prediction models. The...

    Authors: Jiangming Sun, Nina Jeliazkova, Vladimir Chupakhin, Jose-Felipe Golib-Dzib, Ola Engkvist, Lars Carlsson, Jörg Wegner, Hugo Ceulemans, Ivan Georgiev, Vedrin Jeliazkov, Nikolay Kochev, Thomas J. Ashby and Hongming Chen

    Citation: Journal of Cheminformatics 2017 9:17

    Content type: Database

    Published on:

    The Erratum to this article has been published in Journal of Cheminformatics 2017 9:41

  39. Drug–drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigo...

    Authors: Takako Takeda, Ming Hao, Tiejun Cheng, Stephen H. Bryant and Yanli Wang

    Citation: Journal of Cheminformatics 2017 9:16

    Content type: Research article

    Published on:

  40. Isotopic labeling is an analytic technique that is used to track the movement of isotopes through reaction networks. In general, the applicability of isotopic labeling techniques is limited to the investigatio...

    Authors: Mark I. Borkum, Patrick N. Reardon, Ronald C. Taylor and Nancy G. Isern

    Citation: Journal of Cheminformatics 2017 9:14

    Content type: Research article

    Published on:

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