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Chemistry

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  • Andersen, J. L., Flamm, C., Merkle, D., & Stadler, P. F. (2014). Generic strategies for chemical space exploration. International Journal of Computational Biology and Drug Design, 7(2–3), 225–258.

    Article  Google Scholar 

  • Araki, M., Gutteridge, A., Honda, W., Kanehisa, M., & Yamanishi, Y. (2008). Prediction of drug–target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 24(13), i232–i240.

    Article  Google Scholar 

  • Banck, M., Hutchison, G. R., James, C. A., Morley, C., O’Boyle, N. M., & Vandermeersch, T. (2011). Open Babel: An open chemical toolbox. Journal of Cheminformatics, 3, 33.

    Article  Google Scholar 

  • Barge, L. M., Cardoso, S. S., Cartwright, J. H., Cooper, G. J., Cronin, L., Doloboff, I. J., Escribano, B., Goldstein, R. E., Haudin, F., Jones, D. E., Mackay, A. L., Maselko, J., Pagano, J. J., Pantaleone, J., Russell, M. J., Sainz-Díaz, C. I., Steinbock, O., Stone, D. A., Tanimoto, Y., Thomas, N. L., & Wit, A. D. (2015). From chemical gardens to chemobrionics. Chemical Reviews, 115(16), 8652–8703.

    Article  Google Scholar 

  • Barrett, S. J., & Langdon, W. B. (2006). Advances in the application of machine learning techniques in drug discovery, design and development. In A. Tiwari, R. Roy, J. Knowles, E. Avineri, & K. Dahal (Eds.), Applications of soft computing. Advances in intelligent and soft computing (Vol. 36). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Belianinov, A., et al. (2015). Big data and deep data in scanning and electron microscopies: Deriving functionality from multidimensional data sets. Advanced Structural and Chemical Imaging, 1, 6. https://doi.org/10.1186/s40679-015-0006-6.

    Article  Google Scholar 

  • Benz, R. W., Baldi, P., & Swamidass, S. J. (2008). Discovery of power-laws in chemical space. Journal of Chemical Information and Modeling, 48(6), 1138–1151.

    Article  Google Scholar 

  • Bolstad, E. S., Coleman, R. G., Irwin, J. J., Mysinger, M. M., & Sterling, T. (2012). ZINC: A free tool to discover chemistry for biology. Journal of Chemical Information and Modeling, 52(7), 1757–1768.

    Article  Google Scholar 

  • Bolton, E., Bryant, S. H., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Kim, S., Shoemaker, B. A., Thiessen, P. A., Wang, J., Yu, B., & Zhang, J. (2016). PubChem substance and compound databases. Nucleic Acids Research, 44, D1202–D1213.

    Article  Google Scholar 

  • Bon, R. S., & Waldmann, H. (2010). Bioactivity-guided navigation of chemical space. Accounts of Chemical Research, 43(8), 1103–1114.

    Article  Google Scholar 

  • Butte, A., & Chen, B. (2016). Leveraging big data to transform target selection and drug discovery. Clinical Pharmacology and Therapeutics, 99(3), 285–297.

    Article  Google Scholar 

  • Buytaert, W., El-khatib, Y., Macleod, C. J., Reusser, D., & Vitolo, C. (2015). Web technologies for environmental Big Data. Environmental Modelling and Software, 63, 185–198.

    Article  Google Scholar 

  • Clarke, P., Coveney, P. V., Heavens, A. F., Jäykkä, J., Korn, A., Mann, R. G., McEwen, J. D., Ridder, S. D., Roberts, S., Scanlon, T., Shellard, E. P., Yates, J. A., & Royal Society (2016). https://doi.org/10.1098/rsta.2016.0153.

  • Dekker, A., Ennis, M., Hastings, J., Harsha, B., Kale, N., Matos, P. D., Muthukrishnan, V., Owen, G., Steinbeck, C., Turner, S., & Williams, M. (2013). The ChEBI reference database and ontology for biologically relevant chemistry: Enhancements for 2013. Nucleic Acids Research, 41, D456–D463.

    Google Scholar 

  • Edwards, M., Aldea, M., & Belisle, M. (2015). Big Data is changing the environmental sciences. Environmental Perspectives, 1. Available from http://www.exponent.com/files/Uploads/Documents/Newsletters/EP_2015_Vol1.pdf.

  • Ekins, S., Tkachenko, V., & Williams, A. J. (2012). Towards a gold standard: Regarding quality in public domain chemistry databases and approaches to improving the situation. Drug Discovery Today, 17(13–14), 685–701.

    Google Scholar 

  • Frey, J. G., & Bird, C. L. (2011). Web-based services for drug design and discovery. Expert Opinion on Drug Discovery, 6(9), 885–895.

    Article  Google Scholar 

  • Frey, J. G., & Bird, C. L. (2013). Cheminformatics and the semantic web: Adding value with linked data and enhanced provenance. Wiley Interdisciplinary Reviews: Computational Molecular Science, 3(5), 465–481. https://doi.org/10.1002/wcms.1127.

    Article  Google Scholar 

  • Gartner. From the Gartner IT glossary: What is Big Data? Available from https://www.gartner.com/it-glossary/big-data.

  • Gilson, M. K., Liu, T., & Nicola, G. (2012). Public domain databases for medicinal chemistry. Journal of Medicinal Chemistry, 55(16), 6987–7002.

    Article  Google Scholar 

  • Groth, P. T., Gray, A. J., Goble, C. A., Harland, L., Loizou, A., & Pettifer, S. (2014). API-centric linked data integration: The open phacts discovery platform case study. Web Semantics: Science, Services and Agents on the World Wide Web, 29, 12–18.

    Article  Google Scholar 

  • Hall, R. J., Murray, C. W., & Verdonk, M. L. (2017). The fragment network: A chemistry recommendation engine built using a graph database. Journal of Medicinal Chemistry, 60(14), 6440–6450. https://doi.org/10.1021/acs.jmedchem.7b00809.

    Article  Google Scholar 

  • Han, Y., Horlacher, O., Kuhn, S., Luttmann, E., Steinbeck, C., & Willighagen, E. L. (2003). The Chemistry Development Kit (CDK): An open-source Java library for chemo-and bioinformatics. Journal of Chemical Information and Computer Sciences, 43(2), 493–500.

    Article  Google Scholar 

  • Hartung, T. (2016). Making big sense from big data in toxicology by read-across. ALTEX, 33(2), 83–93.

    Article  Google Scholar 

  • Hey, A., Tansley, S., & Tolle, K. (Eds.). (2009). The fourth paradigm, data-intensive scientific discovery. Redmond: Microsoft Research. ISBN 978-0-9825442-0-4.

    Google Scholar 

  • http://generic.wordpress.soton.ac.uk/dial-a-molecule/phase-iii-themes/data-driven-synthesis/. Accessed 30 Oct 2017.

  • https://home.cern/. Accessed 30 Oct 2017.

  • https://lcls.slac.stanford.edu/. Accessed 30 Oct 2017.

  • https://pubchem.ncbi.nlm.nih.gov/. Accessed 30 Oct 2017.

  • https://www.ccdc.cam.ac.uk. Accessed 30 Oct 2017.

  • https://www.nist.gov/mml/acmd/trc/thermoml. Accessed 30 Oct 2017

  • http://www.RDKit.org. Accessed 30 Oct 2017.

  • https://www.rcsb.org/pdb/statistics/holdings.do. Accessed 30 Oct 2017.

  • https://www.xfel.eu/. Accessed 30 Oct 2017.

  • ICIS Chemical Business. (2013). Big data and the chemical industry. Available from https://www.icis.com/resources/news/2013/12/13/9735874/big-data-and-the-chemical-industry/.

  • Jessop, D. M., Adams, S. E., Willighagen, E. L., Hawizy, L., & Murray-Rust, P. (2011). OSCAR4: A flexible architecture for chemical text-mining. Journal of Cheminformatics, 3, 41. https://doi.org/10.1186/1758-2946-3-41.

    Article  Google Scholar 

  • Kaestner, M. (2016). Big Data means big opportunities for chemical companies. KPMG REACTION, 16–29.

    Google Scholar 

  • Lowe, G. (1995). Combinatorial chemistry. Chemical Society Review, 24, 309–317. https://doi.org/10.1039/CS9952400309.

    Article  Google Scholar 

  • Lundia, S. R. (2015). How big data is influencing chemical manufacturing. Available from https://www.chem.info/blog/2015/05/how-big-data-influencing-chemical-manufacturing.

  • Mohimani, H., et al. (2017). Dereplication of peptidic natural products through database search of mass spectra. Nature Chemical Biology, 13, 30–37. https://doi.org/10.1038/nchembio.2219.

    Article  Google Scholar 

  • Pence, H. E., & Williams, A. J. (2016). Big data and chemical education. Journal of Chemical Education, 93(3), 504–508. https://doi.org/10.1021/acs.jchemed.5b00524.

    Article  Google Scholar 

  • Peter V. Coveney, Edward R. Dougherty, Roger R. Highfield, (2016) Big data need big theory too. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374(2080):20160153

    Article  Google Scholar 

  • Ramakrishnan, R., Dral, P. O., Rupp, M., & Anatole von Lilienfeld, O. (2015). Big data meets quantum chemistry approximations: The Δ-machine learning approach. Journal of Chemical Theory and Computation, 11(5), 2087–2096. https://doi.org/10.1021/acs.jctc.5b00099.

    Article  Google Scholar 

  • Reymond, J. (2015). The chemical space project. Accounts of Chemical Research, 48(3), 722–730.

    Article  Google Scholar 

  • Sayle, R. A., Batista, J., & Grant, A. (2013). An efficient maximum common subgraph(MCS) searching of large chemical databases. Journal of Cheminformatics, 5(1), O15. https://doi.org/10.1186/1758-2946-5-S1-O15.

    Article  Google Scholar 

  • Schneider, N., Lowe, D. M., Sayle, R. A., Tarselli, M. A., & Landrum, G. A. (2016). Big data from pharmaceutical patents: A computational analysis of medicinal chemists’ bread and butter. Journal of Medicinal Chemistry, 59(9), 4385–4402. https://doi.org/10.1021/acs.jmedchem.6b00153.

    Article  Google Scholar 

  • Spek, A. L. (2009). Structure validation in chemical crystallography. Acta Crystallographica. Section D, Biological Crystallography.

    Article  Google Scholar 

  • Swain, M. C., & Cole, J. M. (2016). ChemDataExtractor: A toolkit for automated extraction of chemical information from the scientific literature. Journal of Chemical Information and Modeling, 56(10), 1894–1904. https://doi.org/10.1021/acs.jcim.6b00207.

    Article  Google Scholar 

  • SzymaÅ„ski, P., Marcowicz, M., & Mikiciuk-Olasik, E. (2012). Adaptation of high-throughput screening in drug discovery – Toxicological screening tests. International Journal of Molecular Sciences, 13, 427–452. https://doi.org/10.3390/ijms13010427.

    Article  Google Scholar 

  • Tetko, I. V., Engkvist, O., Koch, U., Reymond, J.-L., & Chen, H. (2016). BIGCHEM: Challenges and opportunities for big data analysis in chemistry. Molecular Informatics, 35, 615.

    Article  Google Scholar 

  • Tormay, P. (2015). Big data in pharmaceutical R&D: Creating a sustainable R&D engine. Pharmaceutical Medicine 29(2), 87–92.

    Article  Google Scholar 

  • Whitesides, G. M. (2015). Reinventing chemistry. Angewandte Chemie, 54(11), 3196–3209.

    Article  Google Scholar 

  • Yeguas, V., & Casado, R. (2014). Big Data issues in computational chemistry, 2014 international conference on future internet of things and cloud. Available from http://ieeexplore.ieee.org/abstract/document/6984225/.

  • Zhu, H., et al. (2014). Big data in chemical toxicity research: The use of high-throughput screening assays to identify potential toxicants. Chemical Research in Toxicology, 27(10), 1643–1651. https://doi.org/10.1021/tx500145h.

    Article  Google Scholar 

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Bird, C.L., Frey, J.G. (2018). Chemistry. In: Schintler, L., McNeely, C. (eds) Encyclopedia of Big Data. Springer, Cham. https://doi.org/10.1007/978-3-319-32001-4_260-1

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  • DOI: https://doi.org/10.1007/978-3-319-32001-4_260-1

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