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      NutriChem: a systems chemical biology resource to explore the medicinal value of plant-based foods

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          Abstract

          There is rising evidence of an inverse association between chronic diseases and diets characterized by rich fruit and vegetable consumption. Dietary components may act directly or indirectly on the human genome and modulate multiple processes involved in disease risk and disease progression. However, there is currently no exhaustive resource on the health benefits associated to specific dietary interventions, or a resource covering the broad molecular content of food. Here we present the first release of NutriChem, available at http://cbs.dtu.dk/services/NutriChem-1.0, a database generated by text mining of 21 million MEDLINE abstracts for information that links plant-based foods with their small molecule components and human disease phenotypes. NutriChem contains text-mined data for 18478 pairs of 1772 plant-based foods and 7898 phytochemicals, and 6242 pairs of 1066 plant-based foods and 751 diseases. In addition, it includes predicted associations for 548 phytochemicals and 252 diseases. To the best of our knowledge this database is the only resource linking the chemical space of plant-based foods with human disease phenotypes and provides a foundation for understanding mechanistically the consequences of eating behaviors on health.

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          Open Babel: An open chemical toolbox

          Background A frequent problem in computational modeling is the interconversion of chemical structures between different formats. While standard interchange formats exist (for example, Chemical Markup Language) and de facto standards have arisen (for example, SMILES format), the need to interconvert formats is a continuing problem due to the multitude of different application areas for chemistry data, differences in the data stored by different formats (0D versus 3D, for example), and competition between software along with a lack of vendor-neutral formats. Results We discuss, for the first time, Open Babel, an open-source chemical toolbox that speaks the many languages of chemical data. Open Babel version 2.3 interconverts over 110 formats. The need to represent such a wide variety of chemical and molecular data requires a library that implements a wide range of cheminformatics algorithms, from partial charge assignment and aromaticity detection, to bond order perception and canonicalization. We detail the implementation of Open Babel, describe key advances in the 2.3 release, and outline a variety of uses both in terms of software products and scientific research, including applications far beyond simple format interconversion. Conclusions Open Babel presents a solution to the proliferation of multiple chemical file formats. In addition, it provides a variety of useful utilities from conformer searching and 2D depiction, to filtering, batch conversion, and substructure and similarity searching. For developers, it can be used as a programming library to handle chemical data in areas such as organic chemistry, drug design, materials science, and computational chemistry. It is freely available under an open-source license from http://openbabel.org.
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            Fruit and vegetable consumption and risk of coronary heart disease: a meta-analysis of cohort studies.

            The consumption of fruit and vegetables is associated with a reduced rate of coronary heart disease (CHD) in observational cohorts. The purpose of this study was to assess the strength of this association in a meta-analysis. Cohort studies were selected if they reported relative risks (RRs) and 95% CI for coronary heart disease or mortality and if they presented a quantitative assessment of fruit and vegetable intake. The pooled RRs were calculated for each additional portion of fruit and/or vegetables consumed per day, and the linearity of the associations were examined. Nine studies were eligible for inclusion in the meta-analysis that consisted of 91,379 men, 129,701 women, and 5,007 CHD events. The risk of CHD was decreased by 4% [RR (95% CI): 0.96 (0.93-0.99), P = 0.0027] for each additional portion per day of fruit and vegetable intake and by 7% [0.93 (0.89-0.96), P < 0.0001] for fruit intake. The association between vegetable intake and CHD risk was heterogeneous (P = 0.0043), more marked for cardiovascular mortality [0.74 (0.75-0.84), P < 0.0001] than for fatal and nonfatal myocardial infarction [0.95 (0.92-0.99), P = 0.0058]. Visual inspection of the funnel plot suggested a publication bias, although not statistically significant. Therefore, the reported RRs are probably overestimated. This meta-analysis of cohort studies shows that fruit and vegetable consumption is inversely associated with the risk of CHD. The causal mechanism of this association, however, remains to be demonstrated.
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              Reoptimization of MDL keys for use in drug discovery.

              For a number of years MDL products have exposed both 166 bit and 960 bit keysets based on 2D descriptors. These keysets were originally constructed and optimized for substructure searching. We report on improvements in the performance of MDL keysets which are reoptimized for use in molecular similarity. Classification performance for a test data set of 957 compounds was increased from 0.65 for the 166 bit keyset and 0.67 for the 960 bit keyset to 0.71 for a surprisal S/N pruned keyset containing 208 bits and 0.71 for a genetic algorithm optimized keyset containing 548 bits. We present an overview of the underlying technology supporting the definition of descriptors and the encoding of these descriptors into keysets. This technology allows definition of descriptors as combinations of atom properties, bond properties, and atomic neighborhoods at various topological separations as well as supporting a number of custom descriptors. These descriptors can then be used to set one or more bits in a keyset. We constructed various keysets and optimized their performance in clustering bioactive substances. Performance was measured using methodology developed by Briem and Lessel. "Directed pruning" was carried out by eliminating bits from the keysets on the basis of random selection, values of the surprisal of the bit, or values of the surprisal S/N ratio of the bit. The random pruning experiment highlighted the insensitivity of keyset performance for keyset lengths of more than 1000 bits. Contrary to initial expectations, pruning on the basis of the surprisal values of the various bits resulted in keysets which underperformed those resulting from random pruning. In contrast, pruning on the basis of the surprisal S/N ratio was found to yield keysets which performed better than those resulting from random pruning. We also explored the use of genetic algorithms in the selection of optimal keysets. Once more the performance was only a weak function of keyset size, and the optimizations failed to identify a single globally optimal keyset. Instead multiple, equally optimal keysets could be produced which had relatively low overlap of the descriptors they encoded.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                28 January 2015
                08 August 2014
                08 August 2014
                : 43
                : Database issue , Database issue
                : D940-D945
                Affiliations
                [1 ]Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet, Building 208, DK-2800 Lyngby, Denmark
                [2 ]School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +45 4525 6162; Fax: +45 4593 15 85; Email: irene@ 123456cbs.dtu.dk
                Article
                10.1093/nar/gku724
                4383999
                25106869
                056f3e68-f8e8-45bc-bb03-30711caf7139
                © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 28 July 2014
                : 12 July 2014
                : 01 June 2014
                Page count
                Pages: 6
                Categories
                Database Issue
                Custom metadata
                28 January 2015

                Genetics
                Genetics

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