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Chemical Informatics Functionality in R

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  • Guha, Rajarshi

Abstract

The flexibility and scope of the R programming environment has made it a popular choice for statistical modeling and scientific prototyping in a number of fields. In the field of chemistry, R provides several tools for a variety of problems related to statistical modeling of chemical information. However, one aspect common to these tools is that they do not have direct access to the information that is available from chemical structures, such as contained in molecular descriptors. We describe the rcdk package that provides the R user with access to the CDK, a Java framework for cheminformatics. As a result, it is possible to read in a variety of molecular formats, calculate molecular descriptors and evaluate fingerprints. In addition, we describe the rpubchem that will allow access to the data in PubChem, a public repository of molecular structures and associated assay data for approximately 8 million compounds. Currently, the package allows access to structural information as well as some simple molecular properties from PubChem. In addition the package allows access to bio-assay data from the PubChem FTP servers.

Suggested Citation

  • Guha, Rajarshi, 2007. "Chemical Informatics Functionality in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 18(i05).
    • Handle: RePEc:jss:jstsof:v:018:i05
    DOI: http://hdl.handle.net/10.18637/jss.v018.i05
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    Cited by:

    1. Mullen, Katharine M. & van Stokkum, Ivo H. M., 2007. "An Introduction to the "Special Volume Spectroscopy and Chemometrics in R"," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 18(i01).
    2. Jenna E. Leeuwen & Wail Ba-Alawi & Emily Branchard & Jennifer Cruickshank & Wiebke Schormann & Joseph Longo & Jennifer Silvester & Peter L. Gross & David W. Andrews & David W. Cescon & Benjamin Haibe-, 2022. "Computational pharmacogenomic screen identifies drugs that potentiate the anti-breast cancer activity of statins," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    3. Anna Cichonska & Balaguru Ravikumar & Elina Parri & Sanna Timonen & Tapio Pahikkala & Antti Airola & Krister Wennerberg & Juho Rousu & Tero Aittokallio, 2017. "Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-28, August.
    4. Aleksandr Ianevski & Kristen Nader & Kyriaki Driva & Wojciech Senkowski & Daria Bulanova & Lidia Moyano-Galceran & Tanja Ruokoranta & Heikki Kuusanmäki & Nemo Ikonen & Philipp Sergeev & Markus Vähä-Ko, 2024. "Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    5. Alina Bărbulescu & Lucica Barbeș & Cristian Ștefan Dumitriu, 2022. "Computer-Aided Methods for Molecular Classification," Mathematics, MDPI, vol. 10(9), pages 1-19, May.
    6. repec:jss:jstsof:18:i01 is not listed on IDEAS

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