The first paper this week is a QSAR paper. In fact, it does some interesting benchmarking of a few tools with a data set of about 6000 compounds. It includes looking into the applicability domain, and studies the error of prediction for compounds inside and outside the chemical space defined by the training set. The paper indirectly uses the CDK descriptor calculation corner, by using EPA’s T.E.S.T. toolkit (at least one author, Todd Martin, contributed to the CDK).
Callahan, A., Cruz-Toledo, J., Dumontier, M., Apr. 2013. Ontology-Based querying with Bio2RDF’s linked open data. Journal of Biomedical Semantics 4 (Suppl 1), S1+. URL http://dx.doi.org/10.1186/2041-1480-4-s1-s1
Arvind et al. study tetranortriterpenoids using a QSAR approach involving COMFA and the CPSA descriptor (green OA PDF). The latter CDK descriptor is calculated using Bioclipse. The study finds that using compound classes can improve the regression.
Arvind, K., Anand Solomon, K., Rajan, S. S., Apr. 2013. QSAR studies of tetranortriterpenoid: An analysis through CoMFA and CPSA parameters. Letters in Drug Design & Discovery 10 (5), 427-436. URL http://dx.doi.org/10.2174/1570180811310050010
Accurate monoisotopic masses
Another useful application of the CDK is the Java wrapping of the isotope data in the Blue Obelisk Data Repository (BODR). Mareile Niesser et al. use Rajarshi’s rcdk package for R to calculate the differences in accurate monoisotopic masses. They do not cite the CDK directly, but do mention it by name in the text.
Niesser, M., Harder, U., Koletzko, B., Peissner, W., Jun. 2013. Quantification of urinary folate catabolites using liquid chromatography–tandem mass spectrometry. Journal of Chromatography B 929, 116-124. URL http://dx.doi.org/10.1016/j.jchromb.2013.04.008