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These utilities can be enabled by installing the ChemmineOB package and the OpenBabel software itself. Plot(as.dendrogram(hc), edgePar=list(col=4, lwd=2), horiz=TRUE) # Plot hierarchical clustering treeĬhemmineR integrates now a subset of cheminformatics functionalities implemented in the OpenBabel C++ library. Hc <- hclust(as.dist(1-simMA), method="single") # Hierarchical clustering with simMA as input SimMA <- sapply(cid(fpset), function(x) fpSim(fpset, fpset, sorted=FALSE)) # Compute similarity matrix # Structure similarity searching and clustering using PubChem fingerprintsĭata(sdfsample) sdfset <- sdfsample cid(sdfset) <- sdfid(sdfset) # Refresh sample data setįpset <- fp2bit(sdfset) # Convert base 64 encoded fingerprints to binary matrixįpSim(fpset, fpset) # Pairwise compound structure comparisonsįpSim(fpset, fpset) # Structure similarity searching # Structure similarity searching and clustering using atom pairsĪpset <- sdf2ap(sdfset) # Generate atom pair descriptor database for searchingĭata(apset) # Loads same data set provided by library.Ĭmp.search(apset, apset, type=3, cutoff = 0.3) # Search apset database with single compound.Ĭmp.cluster(db=apset, cutoff = c(0.65, 0.5)) # Binning clustering using variable similarity cutoffs. Sdf.visualize(sdfset) # viewing in web browser Plot(sdfset) # plots to R graphics device # Plot molecule structure of one or many SDFs # write.SDF(sdfset, file="sub.sdf", sig=TRUE) GrepSDFset("650001", sdfset, field="datablock", mode="subset") # To return index, set mode="index") Propma <- ame(MF=MF(sdfset), MW=MW(sdfset), atomcountMA(sdfset)) # Compute atom frequency matrix, molecular weight and formula Numchar <- splitNumChar(blockmatrix=blockmatrix) # Splits to numeric and character matrix # Converting the data blocks in SDFset to matrixīlockmatrix <- datablock2ma(datablocklist=datablock(sdfset)) # Converts data block to matrix # Assigning compound IDs and keeping them unique # Miscellaneous accessor methods for SDFset container # The SDFset object is created during the import of an SD file # Sample data set contains first 100 compounds from PubChem SD file "Compound_00650001_" Since then many additional utilities and add-on packages have been added to the environment and many more are under development for future releases ( Backman et al., 2011, Wang et al., 2013). The first version of this package was published in 2008 in Bioinformatics: 24, 1733-1734 (please cite where appropriate). The integration of chemoinformatic tools with the R programming environment has many advantages, such as easy access to a wide spectrum of statistical methods, machine learning algorithms and graphic utilities. In addition, it offers visualization functions for compound clustering results and chemical structures. Its latest version contains functions for efficient processing of large numbers of molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries with a wide spectrum of algorithms. PDF ] Slides: ĬhemmineR is a cheminformatics package for analyzing drug-like small molecule data in R.
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However, the template function will be re-compiled every time render is called, which might impact performance. Pug also provides the pug.render() family of functions that combine compiling and rendering into one step. - template.pugĬonst compiledFunction = pug.compileFile('template.pug') The compiled function can be re-used, and called with different sets of data. Call that resultant function with your data, and voilà!, it will return a string of HTML rendered with your data.
PUBCHEM PUG DOCUMENTATION CODE
pug.compile() will compile the Pug source code into a JavaScript function that takes a data object (called “ locals”) as an argument.
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The general rendering process of Pug is simple.
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Pug is available via npm: $ npm install pug