ChemmineR environment with its add-on packages and selected functionalities
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ChemmineR is a cheminformatics package for analyzing drug-like small molecule data in R. Its latest version contains functions for efficient processing of large numbers of small molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries with a wide spectrum of algorithms.
ChemmineR environment with its add-on packages and selected functionalities
In addition, ChemmineR offers visualization functions for compound clustering results and chemical structures. 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. The first version of this package was published in Cao, Charisi, Cheng, Jiang, and Girke (2008) . Since then many additional utilities and add-on packages have been added to the environment and many more are under development for future releases (Figure 2; (Backman, Cao, and Girke, 2011; Wang, Backman, Horan, and Girke, 2013)).
Improved SMILES support via new SMIset object class and SMILES import/export functions
Integration of a subset of OpenBabel functionalities via new ChemmineOB add-on package (Cao, Charisi, Cheng, et al., 2008)
Streaming functionality for processing millions of molecules on a laptop
Mismatch tolerant maximum common substructure (MCS) search algorithm
Fast and memory efficient fingerprint search support using atom pair or PubChem fingerprints
The R software for running ChemmineR can be downloaded from CRAN (http://cran.at.r-project.org/). The ChemmineR package can be installed from R using the bioLite install command.
source("http://bioconductor.org/biocLite.R") # Sources the biocLite.R installation script.
biocLite("ChemmineR") # Installs the package. library("ChemmineR") # Loads the package library(help="ChemmineR") # Lists all functions and classes
vignette("ChemmineR") # Opens this PDF manual from R The following code gives an overview of the most important functionalities provided by ChemmineR. Copy and paste of the commands into the R console will demonstrate their utilities.
Create Instances of SDFset class:
data(sdfsample)
sdfset <- sdfsample
sdfset # Returns summary of SDFset ## An instance of "SDFset" with 100 molecules sdfset[1:4] # Subsetting of object## An instance of "SDFset" with 4 molecules sdfset[[1]] # Returns summarized content of one SDF## An instance of "SDF"
##
## <<header>>
## Molecule_Name
## "650001"
## Source
## " -OEChem-07071010512D"
## Comment
## ""
## Counts_Line
## " 61 64 0 0 0 0 0 0 0999 V2000"
##
## <<atomblock>>
## C1 C2 C3 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16
## O_1 7.0468 0.0839 0 0 0 0 0 0 0 0 0 0 0 0 0
## O_2 12.2708 1.0492 0 0 0 0 0 0 0 0 0 0 0 0 0
## ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
## H_60 1.8411 -1.5985 0 0 0 0 0 0 0 0 0 0 0 0 0
## H_61 2.6597 -1.2843 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## <<bondblock>>
## C1 C2 C3 C4 C5 C6 C7
## 1 1 16 2 0 0 0 0
## 2 2 23 1 0 0 0 0
## ... ... ... ... ... ... ... ...
## 63 33 60 1 0 0 0 0
## 64 33 61 1 0 0 0 0
##
## <<datablock>> (33 data items)
## PUBCHEM_COMPOUND_CID PUBCHEM_COMPOUND_CANONICALIZED
## "650001" "1"
## PUBCHEM_CACTVS_COMPLEXITY PUBCHEM_CACTVS_HBOND_ACCEPTOR
## "700" "7"
##
## "..." view(sdfset[1:4]) # Returns summarized content of many SDFs, not printed here
as(sdfset[1:4], "list") # Returns complete content of many SDFs, not printed here An SDFset is created during the import of an SD file:
sdfset <- read.SDFset("http://faculty.ucr.edu/ tgirke/Documents/R_BioCond/Samples/sdfsample.sdf") Miscellaneous accessor methods for SDFset container:
header(sdfset[1:4]) # Not printed here header(sdfset[[1]])## Molecule_Name
## "650001"
## Source
## " -OEChem-07071010512D"
## Comment
## ""
## Counts_Line
## " 61 64 0 0 0 0 0 0 0999 V2000" atomblock(sdfset[1:4]) # Not printed here atomblock(sdfset[[1]])[1:4,] ## C1 C2 C3 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16
## O_1 7.047 0.0839 0 0 0 0 0 0 0 0 0 0 0 0 0
## O_2 12.271 1.0492 0 0 0 0 0 0 0 0 0 0 0 0 0
## O_3 12.271 3.1186 0 0 0 0 0 0 0 0 0 0 0 0 0
## O_4 7.913 2.5839 0 0 0 0 0 0 0 0 0 0 0 0 0bondblock(sdfset[1:4]) # Not printed here bondblock(sdfset[[1]])[1:4,] ## C1 C2 C3 C4 C5 C6 C7
## 1 1 16 2 0 0 0 0
## 2 2 23 1 0 0 0 0
## 3 2 27 1 0 0 0 0
## 4 3 25 1 0 0 0 0 datablock(sdfset[1:4]) # Not printed here datablock(sdfset[[1]])[1:4] ## PUBCHEM_COMPOUND_CID PUBCHEM_COMPOUND_CANONICALIZED
## "650001" "1"
## PUBCHEM_CACTVS_COMPLEXITY PUBCHEM_CACTVS_HBOND_ACCEPTOR
## "700" "7"Assigning compound IDs and keeping them unique:
cid(sdfset)[1:4] # Returns IDs from SDFset object## [1] "CMP1" "CMP2" "CMP3" "CMP4" sdfid(sdfset)[1:4] # Returns IDs from SD file header block## [1] "650001" "650002" "650003" "650004" unique_ids <- makeUnique(sdfid(sdfset))## [1] "No duplicates detected!" cid(sdfset) <- unique_ids Converting the data blocks in an SDFset to a matrix:
blockmatrix <- datablock2ma(datablocklist=datablock(sdfset)) # Converts data block to matrix
numchar <- splitNumChar(blockmatrix=blockmatrix) # Splits to numeric and character matrix
numchar[[1]][1:2,1:2] # Slice of numeric matrix ## PUBCHEM_COMPOUND_CID PUBCHEM_COMPOUND_CANONICALIZED
## 650001 650001 1
## 650002 650002 1 numchar[[2]][1:2,10:11] # Slice of character matrix ## PUBCHEM_MOLECULAR_FORMULA
## 650001 "C23H28N4O6"
## 650002 "C18H23N5O3"
## PUBCHEM_OPENEYE_CAN_SMILES
## 650001 "CC1=CC(=NO1)NC(=O)CCC(=O)N(CC(=O)NC2CCCC2)C3=CC4=C(C=C3)OCCO4"
## 650002 "CN1C2=C(C(=O)NC1=O)N(C(=N2)NCCCO)CCCC3=CC=CC=C3"Compute atom frequency matrix, molecular weight and formula:
propma <- data.frame(MF=MF(sdfset), MW=MW(sdfset), atomcountMA(sdfset))
propma[1:4, ] ## MF MW C H N O S F Cl
## 650001 C23H28N4O6 456.5 23 28 4 6 0 0 0
## 650002 C18H23N5O3 357.4 18 23 5 3 0 0 0
## 650003 C18H18N4O3S 370.4 18 18 4 3 1 0 0
## 650004 C21H27N5O5S 461.5 21 27 5 5 1 0 0Assign matrix data to data block:
datablock(sdfset) <- propma
datablock(sdfset[1]) ## $`650001`
## MF MW C H N
## "C23H28N4O6" "456.5" "23" "28" "4"
## O S F Cl
## "6" "0" "0" "0"String searching in SDFset ():
grepSDFset("650001", sdfset, field="datablock", mode="subset") # Returns summary view of matches. Not printed here. grepSDFset("650001", sdfset, field="datablock", mode="index") ## 1 1 1 1 1 1 1 1 1
## 1 2 3 4 5 6 7 8 9Export SDFset to SD file:
write.SDF(sdfset[1:4], file="sub.sdf", sig=TRUE) Plot molecule structure of one or many SDFs:
plot(sdfset[1:4], print=FALSE) # Plots structures to R graphics device 
plot of chunk plotstruct
sdf.visualize(sdfset[1:4]) # Compound viewing in web browser 
Visualization webpage created by calling sdf.visualize.
Structure similarity searching and clustering:
apset <- sdf2ap(sdfset) # Generate atom pair descriptor database for searching data(apset) # Load sample apset data provided by library.
cmp.search(apset, apset[1], type=3, cutoff = 0.3, quiet=TRUE) # Search apset database with single compound. ## index cid scores
## 1 1 650001 1.0000
## 2 96 650102 0.3517
## 3 67 650072 0.3118
## 4 88 650094 0.3095
## 5 15 650015 0.3011 cmp.cluster(db=apset, cutoff = c(0.65, 0.5), quiet=TRUE)[1:4,] # Binning clustering using variable similarity cutoffs. ##
## sorting result...## ids CLSZ_0.65 CLID_0.65 CLSZ_0.5 CLID_0.5
## 48 650049 2 48 2 48
## 49 650050 2 48 2 48
## 54 650059 2 54 2 54
## 55 650060 2 54 2 54ChemmineR integrates now a subset of cheminformatics functionalities implemented in the OpenBabel C++ library (O’Boyle, Morley, and Hutchison, 2008; Cao, Charisi, Cheng, et al., 2008) . These utilities can be accessed by installing the ChemmineOB package and the OpenBabel software itself. ChemmineR will automatically detect the availability of ChemmineOB and make use of the additional utilities. The following lists the functions and methods that make use of OpenBabel. References are included to locate the sections in the manual where the utility and usage of these functions is described.
Structure format interconversions (see Section Format Inter-Conversions)
smiles2sdf: converts from SMILES to SDF object
sdf2smiles: converts from SDF to SMILES object
convertFormat: converts strings between two formats
convertFormatFile: converts files between two formats
propOB: generates several compound properties. See the man page for a current list of properties computed.
propOB(sdfset[1])## Note: the specification for class "RSWIGStruct" in package 'ChemmineOB' seems equivalent to one from package 'ChemmineR': not turning on duplicate class definitions for this class.## cansmi
## 650001 O=C(Nc1noc(c1)C)CCC(=O)N(c1ccc2c(c1)OCCO2)CC(=O)NC1CCCC1
## cansmiNS formula
## 650001 O=C(Nc1noc(c1)C)CCC(=O)N(c1ccc2c(c1)OCCO2)CC(=O)NC1CCCC1 C23H28N4O6
## title HBA1 HBA2 HBD logP MR MW nF TPSA
## 650001 650001 37 10 2 3.029 119.9 456.5 0 123fingerprintOB: generates fingerprints for compounds. The fingerprint name can be anything supported by OpenBabel. See the man page for a current list.
fingerprintOB(sdfset,"FP2")## An instance of a 1024 bit "FPset" of type "FP2" with 100 moleculessmartsSearchOB: find matches of SMARTS patterns in compounds
#count rotable bonds
smartsSearchOB(sdfset[1:5],"[!$(*#*)&!D1]-!@[!$(*#*)&!D1]",uniqueMatches=FALSE)## 650001 650002 650003 650004 650005
## 24 20 14 30 10The following list gives an overview of the most important S4 classes, methods and functions available in the ChemmineR package. The help documents of the package provide much more detailed information on each utility. The standard R help documents for these utilities can be accessed with this syntax: ?function_name (e.g. ?cid) and ?class_name-class (e.g. ?“SDFset-class”).
Classes
SDFstr: intermediate string class to facilitate SD file import; not important for end user
SDF: container for single molecule imported from an SD file
SDFset: container for many SDF objects; most important structure container for end user
SMI: container for a single SMILES string
SMIset: container for many SMILES strings
Functions/Methods (mainly for SDFset container, SMIset should be coerced with smiles2sdf to SDFset)
Accessor methods for SDF/SDFset
Object slots: cid, header, atomblock, bondblock, datablock (sdfid, datablocktag)
Summary of SDFset: view
Matrix conversion of data block: datablock2ma, splitNumChar
String search in SDFset: grepSDFset
Coerce one class to another
Utilities
Atom frequencies: atomcountMA, atomcount
Molecular weight: MW
Molecular formula: MF
…
Compound structure depictions
R graphics device: plot, plotStruc
Online: cmp.visualize
Classes
AP: container for atom pair descriptors of a single molecule
APset: container for many AP objects; most important structure descriptor container for end user
FP: container for fingerprint of a single molecule
FPset: container for fingerprints of many molecules, most important structure descriptor container for end user
Functions/Methods
Create AP/APset instances
From SDFset: sdf2ap
From SD file: cmp.parse
Summary of AP/APset: view, db.explain
Accessor methods for AP/APset
Coerce one class to another
Structure Similarity comparisons and Searching
Compute pairwise similarities : cmp.similarity, fpSim
Search APset database: cmp.search, fpSim
AP-based Structure Similarity Clustering
Single-linkage binning clustering: cmp.cluster
Visualize clustering result with MDS: cluster.visualize
The following gives an overview of the most important import/export functionalities for small molecules provided by ChemmineR. The given example creates an instance of the SDFset class using as sample data set the first 100 compounds from this PubChem SD file (SDF): Compound_00650001_00675000.sdf.gz (ftp://ftp.ncbi.nih.gov/pubchem/Compound/CURRENT-Full/SDF/).
SDFs can be imported with the read.SDFset function:
sdfset <- read.SDFset("http://faculty.ucr.edu/ tgirke/Documents/R_BioCond/Samples/sdfsample.sdf") data(sdfsample) # Loads the same SDFset provided by the library
sdfset <- sdfsample
valid <- validSDF(sdfset) # Identifies invalid SDFs in SDFset objects
sdfset <- sdfset[valid] # Removes invalid SDFs, if there are any Import SD file into SDFstr container:
r sdfstr <- read.SDFstr("http://faculty.ucr.edu/ tgirke/Documents/R_BioCond/Samples/sdfsample.sdf") Create SDFset from SDFstr class:
sdfstr <- as(sdfset, "SDFstr")
sdfstr## An instance of "SDFstr" with 100 molecules as(sdfstr, "SDFset") ## An instance of "SDFset" with 100 moleculesThe read.SMIset function imports one or many molecules from a SMILES file and stores them in a SMIset container. The input file is expected to contain one SMILES string per row with tab-separated compound identifiers at the end of each line. The compound identifiers are optional.
Create sample SMILES file and then import it:
data(smisample); smiset <- smisample
write.SMI(smiset[1:4], file="sub.smi")
smiset <- read.SMIset("sub.smi")Inspect content of SMIset:
data(smisample) # Loads the same SMIset provided by the library
smiset <- smisample
smiset ## An instance of "SMIset" with 100 molecules view(smiset[1:2]) ## $`650001`
## An instance of "SMI"
## [1] "O=C(NC1CCCC1)CN(c1cc2OCCOc2cc1)C(=O)CCC(=O)Nc1noc(c1)C"
##
## $`650002`
## An instance of "SMI"
## [1] "O=c1[nH]c(=O)n(c2nc(n(CCCc3ccccc3)c12)NCCCO)C"Accessor functions:
cid(smiset[1:4]) ## [1] "650001" "650002" "650003" "650004" smi <- as.character(smiset[1:2])Create SMIset from named character vector:
as(smi, "SMIset") ## An instance of "SMIset" with 2 moleculesWrite objects of classes SDFset/SDFstr/SDF to SD file:
write.SDF(sdfset[1:4], file="sub.sdf") Writing customized SDFset to file containing ChemmineR signature, IDs from SDFset and no data block:
write.SDF(sdfset[1:4], file="sub.sdf", sig=TRUE, cid=TRUE, db=NULL) Example for injecting a custom matrix/data frame into the data block of an SDFset and then writing it to an SD file:
props <- data.frame(MF=MF(sdfset), MW=MW(sdfset), atomcountMA(sdfset))
datablock(sdfset) <- props
write.SDF(sdfset[1:4], file="sub.sdf", sig=TRUE, cid=TRUE) Indirect export via SDFstr object:
sdf2str(sdf=sdfset[[1]], sig=TRUE, cid=TRUE) # Uses default components
sdf2str(sdf=sdfset[[1]], head=letters[1:4], db=NULL) # Uses custom components for header and data block Write SDF, SDFset or SDFstr classes to file:
write.SDF(sdfset[1:4], file="sub.sdf", sig=TRUE, cid=TRUE, db=NULL)
write.SDF(sdfstr[1:4], file="sub.sdf")
cat(unlist(as(sdfstr[1:4], "list")), file="sub.sdf", sep="") Write objects of class SMIset to SMILES file with and without compound identifiers:
data(smisample); smiset <- smisample # Sample data set
write.SMI(smiset[1:4], file="sub.smi", cid=TRUE) write.SMI(smiset[1:4], file="sub.smi", cid=FALSE) The sdf2smiles and smiles2sdf functions provide format interconversion between SMILES strings (Simplified Molecular Input Line Entry Specification) and SDFset containers.
Convert an SDFset container to a SMILES character string:
data(sdfsample);
sdfset <- sdfsample[1]
smiles <- sdf2smiles(sdfset)
smiles Convert a SMILES character string to an SDFset container:
sdf <- smiles2sdf("CC(=O)OC1=CC=CC=C1C(=O)O")
view(sdf) When the ChemineOB package is installed these conversions are performed with the OpenBabel Open Source Chemistry Toolbox. Otherwise the functions will fall back to using the ChemMine Tools web service for this operation. The latter will require internet connectivity and is limited to only the first compound given. ChemmineOB provides access to the compound format conversion functions of OpenBabel. Currently, over 160 formats are supported by OpenBabel. The functions convertFormat and convertFormatFile can be used to convert files or strings between any two formats supported by OpenBabel. For example, to convert a SMILES string to an SDF string, one can use the convertFormat function.
sdfStr <- convertFormat("SMI","SDF","CC(=O)OC1=CC=CC=C1C(=O)O_name") This will return the given compound as an SDF formatted string. 2D coordinates are also computed and included in the resulting SDF string.
To convert a file with compounds encoded in one format to another format, the convertFormatFile function can be used instead.
convertFormatFile("SMI","SDF","test.smiles","test.sdf") To see the whole list of file formats supported by OpenBabel, one can run from the command-line “obabel -L formats”.
The following write.SDFsplit function allows to split SD Files into any number of smaller SD Files. This can become important when working with very big SD Files. Users should note that this function can output many files, thus one should run it in a dedicated directory!
Create sample SD File with 100 molecules:
write.SDF(sdfset, "test.sdf") Read in sample SD File. Note: reading file into SDFstr is much faster than into SDFset:
sdfstr <- read.SDFstr("test.sdf") Run export on SDFstr object:
write.SDFsplit(x=sdfstr, filetag="myfile", nmol=10) # 'nmol' defines the number of molecules to write to each file Run export on SDFset object:
write.SDFsplit(x=sdfset, filetag="myfile", nmol=10) The sdfStream function allows to stream through SD Files with millions of molecules without consuming much memory. During this process any set of descriptors, supported by ChemmineR, can be computed (e.g. atom pairs, molecular properties, etc.), as long as they can be returned in tabular format. In addition to descriptor values, the function returns a line index that gives the start and end positions of each molecule in the source SD File. This line index can be used by the downstream read.SDFindex function to retrieve specific molecules of interest from the source SD File without reading the entire file into R. The following outlines the typical workflow of this streaming functionality in ChemmineR.
Create sample SD File with 100 molecules:
write.SDF(sdfset, "test.sdf") Define descriptor set in a simple function:
desc <- function(sdfset)
cbind(SDFID=sdfid(sdfset),
# datablock2ma(datablocklist=datablock(sdfset)),
MW=MW(sdfset),
groups(sdfset), APFP=desc2fp(x=sdf2ap(sdfset), descnames=1024,
type="character"), AP=sdf2ap(sdfset, type="character"), rings(sdfset,
type="count", upper=6, arom=TRUE) ) Run sdfStream with desc function and write results to a file called matrix.xls:
sdfStream(input="test.sdf", output="matrix.xls", fct=desc, Nlines=1000) # 'Nlines': number of lines to read from input SD File at a time One can also start reading from a specific line number in the SD file. The following example starts at line number 950. This is useful for restarting and debugging the process. With append=TRUE the result can be appended to an existing file.
sdfStream(input="test.sdf", output="matrix2.xls", append=FALSE, fct=desc, Nlines=1000, startline=950) Select molecules meeting certain property criteria from SD File using line index generated by previous sdfStream step:
indexDF <- read.delim("matrix.xls", row.names=1)[,1:4]
indexDFsub <- indexDF[indexDF$MW < 400, ] # Selects molecules with MW < 400
sdfset <- read.SDFindex(file="test.sdf", index=indexDFsub, type="SDFset") # Collects results in 'SDFset' container Write results directly to SD file without storing larger numbers of molecules in memory:
read.SDFindex(file="test.sdf", index=indexDFsub, type="file",
outfile="sub.sdf") Read AP/APFP strings from file into APset or FP object:
apset <- read.AP(x="matrix.xls", type="ap", colid="AP")
apfp <- read.AP(x="matrix.xls", type="fp", colid="APFP") Alternatively, one can provide the AP/APFP strings in a named character vector:
apset <- read.AP(x=sdf2ap(sdfset[1:20], type="character"), type="ap")
fpchar <- desc2fp(sdf2ap(sdfset[1:20]), descnames=1024, type="character")
fpset <- as(fpchar, "FPset") As an alternative to sdfStream, there is now also an option to store data in an SQL database, which then allows for fast queries and compound retrieval. The default database is SQLite, but any other SQL database should work with some minor modifications to the table definitions, which are stored in schema/compounds.SQLite under the ChemmineR package directory. Compounds are stored in their entirety in the databases so there is no need to keep any original data files.
Users can define their own set of compound features to compute and store when loading new compounds. Each of these features will be stored in its own, indexed table. Searches can then be performed using these features to quickly find specific compounds. Compounds can always be retrieved quickly because of the database index, no need to scan a large compound file. In addition to user defined features, descriptors can also be computed and stored for each compound.
A new database can be created with the initDb function. This takes either an existing database connection, or a filename. If a filename is given then an SQLite database connection is created. It then ensures that the required tables exist and creates them if not. The connection object is then returned. This function can be called safely on the same connection or database many times and will not delete any data.
The functions loadSdf and loadSmiles can be used to load compound data from either a file (both) or an SDFset (loadSdf only). The fct parameter should be a function to extract features from the data. It will be handed an SDFset generated from the data being loaded. This may be done in batches, so there is no guarantee that the given SDFSset will contain the whole dataset. This function should return a data frame with a column for each feature and a row for each compound given. The order of the final data frame should be the same as that of the SDFset. The column names will become the feature names. Each of these features will become a new, indexed, table in the database which can be used later to search for compounds.
The descriptors parameter can be a function which computes descriptors. This function will also be given an SDFset object, which may be done in batches. It should return a data frame with the following two columns: “descriptor” and “descriptor_type”. The “descriptor” column should contain a string representation of the descriptor, and “descriptor_type” is the type of the descriptor. Our convention for atom pair is “ap” and “fp” for finger print. The order should also be maintained.
When the data has been loaded, loadSdf will return the compound id numbers of each compound loaded. These compound id numbers are computed by the database and are not extracted from the compound data itself. They can be used to quickly retrieve compounds later.
New features can also be added using this function. However, all compounds must have all features so if new features are added to a new set of compounds, all existing features must be computable by the fct function given. If new features are detected, all existing compounds will be run through fct in order to compute the new features for them as well.
For example, if dataset X is loaded with features F1 and F2, and then at a later time we load dataset Y with new feature F3, the fct function used to load dataset Y must compute and return features F1, F2, and F3. loadSdf will call fct with both datasets X and Y so that all features are available for all compounds. If any features are missing an error will be raised. If just new features are being added, but no new compounds, use the addNewFeatures function.
In this example, we create a new database called “test.db” and load it with data from an SDFset. We also define fct to compute the molecular weight, “MW”, and the number of rings and aromatic rings. The rings function actually returns a data frame with columns “RINGS” and “AROMATIC”, which will be merged into the data frame being created which will also contain the “MW” column. These will be the names used for these features and must be used when searching with them. Finally, the new compound ids are returned and stored in the “ids” variable.
data(sdfsample)
#create and initialize a new SQLite database
conn <- initDb("test.db")## Loading required package: RSQLite
## Loading required package: DBI # load data and compute 3 features: molecular weight, with the MW function,
# and counts for RINGS and AROMATIC, as computed by rings, which
# returns a data frame itself.
ids<-loadSdf(conn,sdfsample, function(sdfset)
data.frame(MW = MW(sdfset), rings(sdfset,type="count",upper=6, arom=TRUE)) ) By default the loadSdf / loadSmiles functions will detect duplicate compound entries and only insert one of them. This means it is safe to run these functions on the same data set several times and you won’t end up with duplicates. This allows the functions to be re-run in the event that a previous run on a dataset does not complete. Duplicate compounds are detected by compouting the MD5 checksum on the textual representation of it.
It can also update existing compounds with new versions of the same compound. To enable this, set updateByName to true. It will then consider two compounds with the same name to be the same, even if the definition is different. Then, if the name of a compound exists in the database and it is trying to insert another compound with the same name, it will overwrite the existing compound. It will also drop and re-compute all associated descriptors and features for the new compound (assuming the required functions for descriptor and feature computation are available at the time the update is performed).
It is often the case when loading a large set of compounds that several compounds will produce the same descriptor. ChemmineR detects this case and only stores one copy of the descriptor for every compound it is for. This feature saves some space and some time for processes that need to be applied to every descriptor. It also highlights a new problem. If you have a descriptor in hand and you want to find a single compound to represent it, which compound should be used if the descriptor was produced from multiple compounds? To address this problem, ChemmineR allows you to set priority values for each compound-descriptor mapping. Then, in contexts where a single compound is required, the highest priority compound will be chosen. Highest priority corresponds to the lowest numerical value. So mapping with priority 0 would be used first.
To set these priorities there is the function setPriorities. It takes a function, priorityFn, for computing these priority values. The setPriorities function should be run after loading a complete set of data. It will find each group of compounds which share the same descriptor and call the given function, priorityFn, with the compound_id numbers of the group. This function should then assign priorities to each compound-descriptor pair, however it wishes.
One built in priority function is forestSizePriorities. This simply prefers compounds with fewer disconnected components over compounds with more dissconnected components.
setPriorities(conn,forestSizePriorities)Compounds can be searched for using the findCompounds function. This function takes a connection object, a vector of feature names used in the tests, and finally, a vector of tests that must all pass for a compound to be included in the result set. Each test should be a boolean expression. For example: “c(”MW <= 400“,”RINGS > 3“)” would return all compounds with a molecular weight of 400 or less and more than 3 rings, assuming these features exist in the database. The syntax for each test is “<feature name> <SQL operator> <value>”. If you know SQL you can go beyond this basic syntax. These tests will simply be concatenated together with “AND” in-between them and tacked on the end of a WHERE clause of an SQL statement. So any SQL that will work in that context is fine. The function will return a list of compound ids, the actual compounds can be fetched with getCompounds. If just the names are needed, the getCompoundNames function can be used. Compounds can also be fetched by name using the findCompoundsByName function.
In this example we search for compounds with molecular weight less than 300. We then fetch the matching compounds and show their molecular weight.
lightIds <- findCompounds(conn,"MW",c("MW < 300"))
MW(getCompounds(conn,lightIds))## 206 214 217 222 224 228 229 230 233 234 236 237
## 262.3 240.3 265.3 287.4 266.7 278.3 282.3 270.3 223.2 265.3 249.2 285.3
## 242 246 250 255 256 257 268 273 276 282 289 295
## 229.3 248.3 266.3 275.4 299.8 263.4 296.3 275.4 294.4 140.1 294.4 276.3 #names of matching compounds:
getCompoundNames(conn,lightIds) ## [1] "650006" "650014" "650017" "650023" "650025" "650029" "650030"
## [8] "650031" "650034" "650035" "650037" "650038" "650043" "650047"
## [15] "650052" "650060" "650061" "650062" "650073" "650078" "650081"
## [22] "650088" "650095" "650101"Several methods are available to return the different data components of SDF/SDFset containers in batches. The following examples list the most important ones. To save space their content is not printed in the manual.
view(sdfset[1:4]) # Summary view of several molecules
length(sdfset) # Returns number of molecules
sdfset[[1]] # Returns single molecule from SDFset as SDF object
sdfset[[1]][[2]] # Returns atom block from first compound as matrix
sdfset[[1]][[2]][1:4,]
c(sdfset[1:4], sdfset[5:8]) # Concatenation of several SDFsets The grepSDFset function allows string matching/searching on the different data components in SDFset. By default the function returns a SDF summary of the matching entries. Alternatively, an index of the matches can be returned with the setting mode=“index”.
grepSDFset("650001", sdfset, field="datablock", mode="subset") # To return index, set mode="index") Utilities to maintain unique compound IDs:
sdfid(sdfset[1:4]) # Retrieves CMP IDs from Molecule Name field in header block.
cid(sdfset[1:4]) # Retrieves CMP IDs from ID slot in SDFset.
unique_ids <- makeUnique(sdfid(sdfset)) # Creates unique IDs by appending a counter to duplicates.
cid(sdfset) <- unique_ids # Assigns uniquified IDs to ID slot Subsetting by character, index and logical vectors:
view(sdfset[c("650001", "650012")])
view(sdfset[4:1])
mylog <- cid(sdfset)
view(sdfset[mylog]) Accessing SDF/SDFset components: header, atom, bond and data blocks:
atomblock(sdf); sdf[[2]];
sdf[["atomblock"]] # All three methods return the same component
header(sdfset[1:4]) atomblock(sdfset[1:4])
bondblock(sdfset[1:4]) datablock(sdfset[1:4]) header(sdfset[[1]])
atomblock(sdfset[[1]]) bondblock(sdfset[[1]]) datablock(sdfset[[1]]) Replacement Methods:
sdfset[[1]][[2]][1,1] <- 999 atomblock(sdfset)[1] <-
atomblock(sdfset)[2] datablock(sdfset)[1] <- datablock(sdfset)[2] Assign matrix data to data block:
datablock(sdfset) <- as.matrix(iris[1:100,])
view(sdfset[1:4]) Class coercions from SDFstr to list, SDF and SDFset:
as(sdfstr[1:2], "list") as(sdfstr[[1]], "SDF")
as(sdfstr[1:2], "SDFset") Class coercions from SDF to SDFstr, SDFset, list with SDF sub-components:
sdfcomplist <- as(sdf, "list") sdfcomplist <-
as(sdfset[1:4], "list"); as(sdfcomplist[[1]], "SDF") sdflist <-
as(sdfset[1:4], "SDF"); as(sdflist, "SDFset") as(sdfset[[1]], "SDFstr")
as(sdfset[[1]], "SDFset") Class coercions from SDFset to lists with components consisting of SDF or sub-components:
as(sdfset[1:4], "SDF") as(sdfset[1:4], "list") as(sdfset[1:4], "SDFstr")Several methods and functions are available to compute basic compound descriptors, such as molecular formula (MF), molecular weight (MW), and frequencies of atoms and functional groups. In many of these functions, it is important to set addH=TRUE in order to include/add hydrogens that are often not specified in an SD file.
propma <- atomcountMA(sdfset, addH=FALSE)
boxplot(propma, col="blue", main="Atom Frequency") 
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boxplot(rowSums(propma), main="All Atom Frequency") Data frame provided by library containing atom names, atom symbols, standard atomic weights, group and period numbers:
data(atomprop)
atomprop[1:4,] ## Number Name Symbol Atomic_weight Group Period
## 1 1 hydrogen H 1.008 1 1
## 2 2 helium He 4.003 18 1
## 3 3 lithium Li 6.941 1 2
## 4 4 beryllium Be 9.012 2 2Compute MW and formula:
MW(sdfset[1:4], addH=FALSE)## CMP1 CMP2 CMP3 CMP4
## 456.5 357.4 370.4 461.5 MF(sdfset[1:4], addH=FALSE) ## CMP1 CMP2 CMP3 CMP4
## "C23H28N4O6" "C18H23N5O3" "C18H18N4O3S" "C21H27N5O5S"Enumerate functional groups:
groups(sdfset[1:4], groups="fctgroup", type="countMA") ## RNH2 R2NH R3N ROPO3 ROH RCHO RCOR RCOOH RCOOR ROR RCCH RCN
## CMP1 0 2 1 0 0 0 0 0 0 2 0 0
## CMP2 0 2 2 0 1 0 0 0 0 0 0 0
## CMP3 0 1 1 0 1 0 1 0 0 0 0 0
## CMP4 0 1 3 0 0 0 0 0 0 2 0 0Combine MW, MF, charges, atom counts, functional group counts and ring counts in one data frame:
propma <- data.frame(MF=MF(sdfset, addH=FALSE), MW=MW(sdfset, addH=FALSE),
Ncharges=sapply(bonds(sdfset, type="charge"), length),
atomcountMA(sdfset, addH=FALSE),
groups(sdfset, type="countMA"),
rings(sdfset, upper=6, type="count", arom=TRUE))
propma[1:4,] ## MF MW Ncharges C H N O S F Cl RNH2 R2NH R3N ROPO3 ROH
## CMP1 C23H28N4O6 456.5 0 23 28 4 6 0 0 0 0 2 1 0 0
## CMP2 C18H23N5O3 357.4 0 18 23 5 3 0 0 0 0 2 2 0 1
## CMP3 C18H18N4O3S 370.4 0 18 18 4 3 1 0 0 0 1 1 0 1
## CMP4 C21H27N5O5S 461.5 0 21 27 5 5 1 0 0 0 1 3 0 0
## RCHO RCOR RCOOH RCOOR ROR RCCH RCN RINGS AROMATIC
## CMP1 0 0 0 0 2 0 0 4 2
## CMP2 0 0 0 0 0 0 0 3 3
## CMP3 0 1 0 0 0 0 0 4 2
## CMP4 0 0 0 0 2 0 0 3 3The following shows an example for assigning the values stored in a matrix (e.g. property descriptors) to the data block components in an SDFset. Each matrix row will be assigned to the corresponding slot position in the SDFset.
datablock(sdfset) <- propma # Works with all SDF components
datablock(sdfset)[1:4]
test <- apply(propma[1:4,], 1, function(x)
data.frame(col=colnames(propma), value=x))
sdf.visualize(sdfset[1:4], extra = test) The data blocks in SDFs contain often important annotation information about compounds. The datablock2ma function returns this information as matrix for all compounds stored in an SDFset container. The splitNumChar function can then be used to organize all numeric columns in a numeric matrix and the character columns in a character matrix as components of a list object.
datablocktag(sdfset, tag="PUBCHEM_NIST_INCHI")
datablocktag(sdfset,
tag="PUBCHEM_OPENEYE_CAN_SMILES") Convert entire data block to matrix:
blockmatrix <- datablock2ma(datablocklist=datablock(sdfset)) # Converts data block to matrix
numchar <- splitNumChar(blockmatrix=blockmatrix) # Splits matrix to numeric matrix and character matrix
numchar[[1]][1:4,]; numchar[[2]][1:4,]
# Splits matrix to numeric matrix and character matrix Bond matrices provide an efficient data structure for many basic computations on small molecules. The function conMA creates this data structure from SDF and SDFset objects. The resulting bond matrix contains the atom labels in the row/column titles and the bond types in the data part. The labels are defined as follows: 0 is no connection, 1 is a single bond, 2 is a double bond and 3 is a triple bond.
conMA(sdfset[1:2],
exclude=c("H")) # Create bond matrix for first two molecules in sdfset
conMA(sdfset[[1]], exclude=c("H")) # Return bond matrix for first molecule
plot(sdfset[1], atomnum = TRUE, noHbonds=FALSE , no_print_atoms = "", atomcex=0.8) # Plot its structure with atom numbering
rowSums(conMA(sdfset[[1]], exclude=c("H"))) # Return number of non-H bonds for each atomThe function bonds returns information about the number of bonds, charges and missing hydrogens in SDF and SDFset objects. It is used by many other functions (e.g. MW, MF, atomcount, atomcuntMA and plot) to correct for missing hydrogens that are often not specified in SD files.
bonds(sdfset[[1]], type="bonds")[1:4,]## atom Nbondcount Nbondrule charge
## 1 O 2 2 0
## 2 O 2 2 0
## 3 O 2 2 0
## 4 O 2 2 0 bonds(sdfset[1:2], type="charge")## $CMP1
## NULL
##
## $CMP2
## NULL bonds(sdfset[1:2], type="addNH") ## CMP1 CMP2
## 0 0The function rings identifies all possible rings in one or many molecules (here sdfset[1]) using the exhaustive ring perception algorithm from Hanser, Jauffret, and Kaufmann (1996) . In addition, the function can return all smallest possible rings as well as aromaticity information.
The following example returns all possible rings in a list. The argument upper allows to specify an upper length limit for rings. Choosing smaller length limits will reduce the search space resulting in shortened compute times. Note: each ring is represented by a character vector of atom symbols that are numbered by their position in the atom block of the corresponding SDF/SDFset object.
ringatoms <- rings(sdfset[1], upper=Inf, type="all", arom=FALSE, inner=FALSE)For visual inspection, the corresponding compound structure can be plotted with the ring bonds highlighted in color:
atomindex <- as.numeric(gsub(".*_", "", unique(unlist(ringatoms))))
plot(sdfset[1], print=FALSE, colbonds=atomindex) 
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Alternatively, one can include the atom numbers in the plot:
plot(sdfset[1], print=FALSE, atomnum=TRUE, no_print_atoms="H") 
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Aromaticity information of the rings can be returned in a logical vector by setting arom=TRUE:
rings(sdfset[1], upper=Inf, type="all", arom=TRUE, inner=FALSE) ## $RINGS
## $RINGS$ring1
## [1] "N_10" "O_6" "C_32" "C_31" "C_30"
##
## $RINGS$ring2
## [1] "C_12" "C_14" "C_15" "C_13" "C_11"
##
## $RINGS$ring3
## [1] "C_23" "O_2" "C_27" "C_28" "O_3" "C_25"
##
## $RINGS$ring4
## [1] "C_23" "C_21" "C_18" "C_22" "C_26" "C_25"
##
## $RINGS$ring5
## [1] "O_3" "C_28" "C_27" "O_2" "C_23" "C_21" "C_18" "C_22" "C_26" "C_25"
##
##
## $AROMATIC
## ring1 ring2 ring3 ring4 ring5
## TRUE FALSE FALSE TRUE FALSEReturn rings with no more than 6 atoms that are also aromatic:
rings(sdfset[1], upper=6, type="arom", arom=TRUE, inner=FALSE) ## $AROMATIC_RINGS
## $AROMATIC_RINGS$ring1
## [1] "N_10" "O_6" "C_32" "C_31" "C_30"
##
## $AROMATIC_RINGS$ring4
## [1] "C_23" "C_21" "C_18" "C_22" "C_26" "C_25"Count shortest possible rings and their aromaticity assignments by setting type=count and inner=TRUE. The inner (smallest possible) rings are identified by first computing all possible rings and then selecting only the inner rings. For more details, consult the help documentation with ?rings.
rings(sdfset[1:4], upper=Inf, type="count", arom=TRUE, inner=TRUE) ## RINGS AROMATIC
## CMP1 4 2
## CMP2 3 3
## CMP3 4 2
## CMP4 3 3A new plotting function for compound structures has been added to the package recently. This function uses the native R graphics device for generating compound depictions. At this point this function is still in an experimental developmental stage but should become stable soon.
If you have ChemmineOB available you can use the regenCoords option to have OpenBabel regenerate the coordinates for the compound. This can sometimes produce better looking plots.
Plot compound Structures with R’s graphics device:
data(sdfsample)
sdfset <- sdfsample
plot(sdfset[1:4], regenCoords=TRUE,print=FALSE) # 'print=TRUE' returns SDF summaries
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Customized plots:
plot(sdfset[1:4], griddim=c(2,2), print_cid=letters[1:4], print=FALSE,
noHbonds=FALSE) In the following plot, the atom block position numbers in the SDF are printed next to the atom symbols (atomnum = TRUE). For more details, consult help documentation with ?plotStruc or ?plot.
plot(sdfset["CMP1"], atomnum = TRUE, noHbonds=F , no_print_atoms = "",
atomcex=0.8, sub=paste("MW:", MW(sdfsample["CMP1"])), print=FALSE) 
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Substructure highlighting by atom numbers:
plot(sdfset[1], print=FALSE, colbonds=c(22,26,25,3,28,27,2,23,21,18,8,19,20,24)) 
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Alternatively, one can visualize compound structures with a standard web browser using the online ChemMine Tools service. The service allows to display other information next to the structures using the extra argument of the sdf.visualize function. The following examples demonstrate, how one can plot and annotate structures by passing on extra data as vector of character strings, matrices or lists.
Plot structures using web service ChemMine Tools:
sdf.visualize(sdfset[1:4]) Visualization webpage created by calling sdf.visualize.
Add extra annotation as vector:
sdf.visualize(sdfset[1:4], extra=month.name[1:4]) Add extra annotation as matrix:
extra <- apply(propma[1:4,], 1,
function(x) data.frame(Property=colnames(propma), Value=x))
sdf.visualize(sdfset[1:4], extra=extra) Add extra annotation as list:
sdf.visualize(sdfset[1:4], extra=bondblock(sdfset[1:4]))The ChemmineR add-on package provides support for identifying maximum common substructures (MCSs) and flexible MCSs among compounds. The algorithm can be used for pairwise compound comparisons, structure similarity searching and clustering. The manual describing this functionality is available and the associated publication is available in Wang, Backman, Horan, et al. (2013). The following gives a short preview of some functionalities provided by the fmcsR package.
library(fmcsR)
data(fmcstest) # Loads test sdfset object
test <- fmcs(fmcstest[1], fmcstest[2], au=2, bu=1) # Searches for MCS with mismatches
plotMCS(test) # Plots both query compounds with MCS in color 
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The function sdf2ap computes atom pair descriptors for one or many compounds (Carhart, Smith, and Venkataraghavan, 1985; Chen and Reynolds, 2002) . It returns a searchable atom pair database stored in a container of class APset, which can be used for structural similarity searching and clustering. As similarity measure, the Tanimoto coefficient or related coefficients can be used. An APset object consists of one or many AP entries each storing the atom pairs of a single compound. Note: the deprecated cmp.parse function is still available which also generates atom pair descriptor databases, but directly from an SD file. Since the latter function is less flexible it may be discontinued in the future.
Generate atom pair descriptor database for searching:
ap <- sdf2ap(sdfset[[1]]) # For single compound
ap ## An instance of "AP"
## <<atom pairs>>
## 52614450304 52615497856 52615514112 52616547456 52616554624 ... length: 528 apset <- sdf2ap(sdfset)
# For many compounds. view(apset[1:4]) ## $`650001`
## An instance of "AP"
## <<atom pairs>>
## 53688190976 53688190977 53688190978 53688190979 53688190980 ... length: 528
##
## $`650002`
## An instance of "AP"
## <<atom pairs>>
## 53688190976 53688190977 53688190978 53688190979 53689239552 ... length: 325
##
## $`650003`
## An instance of "AP"
## <<atom pairs>>
## 52615496704 53688190976 53688190977 53689239552 53697627136 ... length: 325
##
## $`650004`
## An instance of "AP"
## <<atom pairs>>
## 52617593856 52618642432 52619691008 52619691009 52628079616 ... length: 496Return main components of APset objects:
cid(apset[1:4]) # Compound IDs
ap(apset[1:4]) # Atom pair
descriptors
db.explain(apset[1]) # Return atom pairs in human readable format Coerce APset to other objects:
apset2descdb(apset) # Returns old list-style AP database
tmp <- as(apset, "list") # Returns list
as(tmp, "APset") # Converts list back to APset When working with large data sets it is often desirable to save the SDFset and APset containers as binary R objects to files for later use. This way they can be loaded very quickly into a new R session without recreating them every time from scratch.
Save and load of SDFset and APset containers:
save(sdfset, file = "sdfset.rda", compress = TRUE)
load("sdfset.rda") save(apset, file = "apset.rda", compress = TRUE)
load("apset.rda") The cmp.similarity function computes the atom pair similarity between two compounds using the Tanimoto coefficient as similarity measure. The coefficient is defined as c/(a+b+c), which is the proportion of the atom pairs shared among two compounds divided by their union. The variable c is the number of atom pairs common in both compounds, while a and b are the numbers of their unique atom pairs.
cmp.similarity(apset[1],
apset[2])## [1] 0.2637 cmp.similarity(apset[1], apset[1]) ## [1] 1The cmp.search function searches an atom pair database for compounds that are similar to a query compound. The following example returns a data frame where the rows are sorted by the Tanimoto similarity score (best to worst). The first column contains the indices of the matching compounds in the database. The argument cutoff can be a similarity cutoff, meaning only compounds with a similarity value larger than this cutoff will be returned; or it can be an integer value restricting how many compounds will be returned. When supplying a cutoff of 0, the function will return the similarity values for every compound in the database.
cmp.search(apset,
apset["650065"], type=3, cutoff = 0.3, quiet=TRUE) ## index cid scores ## 1 61 650066 1.0000 ## 2 60 650065 1.0000 ## 3 67 650072 0.3390 ## 4 11 650011 0.3191 ## 5 15 650015 0.3185 ## 6 86 650092 0.3154 ## 7 64 650069 0.3010 Alternatively, the function can return the matches in form of an index or a named vector if the type argument is set to 1 or 2, respectively.
cmp.search(apset, apset["650065"], type=1, cutoff = 0.3, quiet=TRUE)## [1] 61 60 67 11 15 86 64 cmp.search(apset, apset["650065"], type=2, cutoff = 0.3, quiet=TRUE) ## 650066 650065 650072 650011 650015 650092 650069
## 1.0000 1.0000 0.3390 0.3191 0.3185 0.3154 0.3010The FPset class stores fingerprints of small molecules in a matrix-like representation where every molecule is encoded as a fingerprint of the same type and length. The FPset container acts as a searchable database that contains the fingerprints of many molecules. The FP container holds only one fingerprint. Several constructor and coerce methods are provided to populate FP/FPset containers with fingerprints, while supporting any type and length of fingerprints. For instance, the function desc2fp generates fingerprints from an atom pair database stored in an APset, and as(matrix, “FPset”) and as(character, “FPset”) construct an FPset database from objects where the fingerprints are represented as matrix or character objects, respectively.
Show slots of FPset class:
showClass("FPset") ## Class "FPset" [package "ChemmineR"]
##
## Slots:
##
## Name: fpma type foldCount
## Class: matrix character numericInstance of FPset class:
data(apset)
fpset <- desc2fp(apset)
view(fpset[1:2]) ## $`650001`
## An instance of "FP" of type "unknown-9058"
## <<fingerprint>>
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ... length: 1024
##
## $`650002`
## An instance of "FP" of type "unknown-540"
## <<fingerprint>>
## 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 1 ... length: 1024FPset class usage:
fpset[1:4] # behaves like a list ## An instance of a 1024 bit "FPset" of type "apfp" with 4 molecules fpset[[1]] # returns FP object ## An instance of "FP" of type "unknown-7689"
## <<fingerprint>>
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ... length: 1024 length(fpset) # number of compounds ENDCOMMENT## [1] 100 cid(fpset) # returns compound ids ## [1] "650001" "650002" "650003" "650004" "650005" "650006" "650007"
## [8] "650008" "650009" "650010" "650011" "650012" "650013" "650014"
## [15] "650015" "650016" "650017" "650019" "650020" "650021" "650022"
## [22] "650023" "650024" "650025" "650026" "650027" "650028" "650029"
## [29] "650030" "650031" "650032" "650033" "650034" "650035" "650036"
## [36] "650037" "650038" "650039" "650040" "650041" "650042" "650043"
## [43] "650044" "650045" "650046" "650047" "650048" "650049" "650050"
## [50] "650052" "650054" "650056" "650058" "650059" "650060" "650061"
## [57] "650062" "650063" "650064" "650065" "650066" "650067" "650068"
## [64] "650069" "650070" "650071" "650072" "650073" "650074" "650075"
## [71] "650076" "650077" "650078" "650079" "650080" "650081" "650082"
## [78] "650083" "650085" "650086" "650087" "650088" "650089" "650090"
## [85] "650091" "650092" "650093" "650094" "650095" "650096" "650097"
## [92] "650098" "650099" "650100" "650101" "650102" "650103" "650104"
## [99] "650105" "650106" fpset[10] <- 0 # replacement of 10th fingerprint to all zeros
cid(fpset) <- 1:length(fpset) # replaces compound ids
c(fpset[1:4], fpset[11:14]) # concatenation of several FPset objects ## An instance of a 1024 bit "FPset" of type "apfp" with 8 moleculesConstruct FPset class form matrix:
fpma <- as.matrix(fpset) # coerces FPset to matrix
as(fpma, "FPset") ## An instance of a 1024 bit "FPset" of type "unknown-9025" with 100 moleculesConstruct FPset class form character vector:
fpchar <- as.character(fpset) # coerces FPset to character strings
as(fpchar, "FPset") # construction of FPset class from character vector## An instance of a 1024 bit "FPset" of type "apfp" with 100 moleculesCompound similarity searching with FPset:
fpSim(fpset[1], fpset, method="Tanimoto", cutoff=0.4, top=4) ## 1 96 67 15
## 1.0000 0.4719 0.4288 0.4275Folding fingerprints:
fold(fpset) # fold each FP once## An instance of a 512 bit "FPset" of type "apfp" with 100 molecules fold(fpset, count=2) #fold each FP twice## An instance of a 256 bit "FPset" of type "apfp" with 100 molecules fold(fpset, bits=128) #fold each FP down to 128 bits## An instance of a 128 bit "FPset" of type "apfp" with 100 molecules fold(fpset[[1]]) # fold an individual FP## An instance of "FP" of type "unknown-3728"
## <<fingerprint>>
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ... length: 512 fptype(fpset) # get type of FPs## [1] "apfp" numBits(fpset) # get the number of bits of each FP## [1] 1024 foldCount(fold(fpset)) # the number of times an FP or FPset has been folded## [1] 1Atom pairs can be converted into binary atom pair fingerprints of fixed length. Computations on this compact data structure are more time and memory efficient than on their relatively complex atom pair counterparts. The function desc2fp generates fingerprints from descriptor vectors of variable length such as atom pairs stored in APset or list containers. The obtained fingerprints can be used for structure similarity comparisons, searching and clustering.
Create atom pair sample data set:
data(sdfsample)
sdfset <- sdfsample[1:10]
apset <- sdf2ap(sdfset) Compute atom pair fingerprint database using internal atom pair selection containing the 4096 most common atom pairs identified in DrugBank’s compound collection. For details see ?apfp. The following example uses from this set the 1024 most frequent atom pairs:
fpset <- desc2fp(apset, descnames=1024, type="FPset") Alternatively, one can provide any custom atom pair selection. Here, the 1024 most common ones in apset:
fpset1024 <- names(rev(sort(table(unlist(as(apset, "list")))))[1:1024])
fpset <- desc2fp(apset, descnames=fpset1024, type="FPset") A more compact way of storing fingerprints is as character values:
fpchar <- desc2fp(x=apset,
descnames=1024, type="character") fpchar <- as.character(fpset) Converting a fingerprint database to a matrix and vice versa:
fpma <- as.matrix(fpset)
fpset <- as(fpma, "FPset") Similarity searching and returning Tanimoto similarity coefficients:
fpSim(fpset[1], fpset, method="Tanimoto") Under method one can choose from several predefined similarity measures including Tanimoto (default), Euclidean, Tversky or Dice. Alternatively, one can pass on custom similarity functions.
fpSim(fpset[1], fpset, method="Tversky", cutoff=0.4, top=4, alpha=0.5, beta=1) Example for using a custom similarity function:
myfct <- function(a, b, c, d) c/(a+b+c+d)
fpSim(fpset[1], fpset, method=myfct) Clustering example:
simMAap <- sapply(cid(apfpset), function(x) fpSim(x=apfpset[x], apfpset, sorted=FALSE))
hc <- hclust(as.dist(1-simMAap), method="single")
plot(as.dendrogram(hc), edgePar=list(col=4, lwd=2), horiz=TRUE) The fpSim function computes the similarity coefficients (e.g. Tanimoto) for pairwise comparisons of binary fingerprints. For this data type, c is the number of “on-bits” common in both compounds, and a and b are the numbers of their unique “on-bits”. Currently, the PubChem fingerprints need to be provided (here PubChem’s SD files) and cannot be computed from scratch in ChemmineR. The PubChem fingerprint specifications can be loaded with data(pubchemFPencoding).
Convert base 64 encoded PubChem fingerprints to character vector, matrix or FPset object:
cid(sdfset) <- sdfid(sdfset)
fpset <- fp2bit(sdfset, type=1)
fpset <- fp2bit(sdfset, type=2)
fpset <- fp2bit(sdfset, type=3)
fpset ## An instance of a 881 bit "FPset" of type "pubchem" with 100 moleculesPairwise compound structure comparisons:
fpSim(fpset[1], fpset[2]) ## 650002
## 0.5365Similarly, the fpSim function provides search functionality for PubChem fingerprints:
fpSim(fpset["650065"], fpset, method="Tanimoto", cutoff=0.6, top=6) ## 650065 650066 650035 650019 650012 650046
## 1.0000 0.9945 0.7436 0.7432 0.7230 0.7143The cmp.search function allows to visualize the chemical structures for the search results. Similar but more flexible chemical structure rendering functions are plot and sdf.visualize described above. By setting the visualize argument in cmp.search to TRUE, the matching compounds and their scores can be visualized with a standard web browser. Depending on the visualize.browse argument, an URL will be printed or a webpage will be opened showing the structures of the matching compounds along with their scores.
View similarity search results in R’s graphics device:
cid(sdfset) <-
cid(apset) # Assure compound name consistency among objects.
plot(sdfset[names(cmp.search(apset, apset["650065"], type=2, cutoff=4, quiet=TRUE))], print=FALSE) 
plot of chunk search_result
View results online with Chemmine Tools:
similarities <- cmp.search(apset, apset[1], type=3, cutoff = 10)
sdf.visualize(sdfset[similarities[,1]], extra=similarities[,3]) Often it is of interest to identify very similar or identical compounds in a compound set. The cmp.duplicated function can be used to quickly identify very similar compounds in atom pair sets, which will be frequently, but not necessarily, identical compounds.
Identify compounds with identical AP sets:
cmp.duplicated(apset, type=1)[1:4] # Returns AP duplicates as logical vector ## [1] FALSE FALSE FALSE FALSE cmp.duplicated(apset, type=2)[1:4,] # Returns AP duplicates as data frame ## ids CLSZ_100 CLID_100
## 1 650082 1 1
## 2 650059 2 2
## 3 650060 2 2
## 4 650010 1 3Plot the structure of two pairs of duplicates:
plot(sdfset[c("650059","650060", "650065", "650066")], print=FALSE) 
plot of chunk duplicates
Remove AP duplicates from SDFset and APset objects:
apdups <- cmp.duplicated(apset, type=1)
sdfset[which(!apdups)]; apset[which(!apdups)] ## An instance of "SDFset" with 96 molecules## An instance of "APset" with 96 moleculesAlternatively, one can identify duplicates via other descriptor types if they are provided in the data block of an imported SD file. For instance, one can use here fingerprints, InChI, SMILES or other molecular representations. The following examples show how to enumerate by identical InChI strings, SMILES strings and molecular formula, respectively.
count <- table(datablocktag(sdfset,
tag="PUBCHEM_NIST_INCHI"))
count <- table(datablocktag(sdfset, tag="PUBCHEM_OPENEYE_CAN_SMILES"))
count <- table(datablocktag(sdfset, tag="PUBCHEM_MOLECULAR_FORMULA"))
count[1:4] ##
## C10H9FN2O2S C11H12N4OS C11H13NO4 C12H11ClN2OS
## 1 1 1 1Compound libraries can be clustered into discrete similarity groups with the binning clustering function cmp.cluster. The function accepts as input an atom pair (APset) or a fingerprint (FPset) descriptor database as well as a similarity threshold. The binning clustering result is returned in form of a data frame. Single linkage is used for cluster joining. The function calculates the required compound-to-compound distance information on the fly, while a memory-intensive distance matrix is only created upon user request via the save.distances argument (see below).
Because an optimum similarity threshold is often not known, the cmp.cluster function can calculate cluster results for multiple cutoffs in one step with almost the same speed as for a single cutoff. This can be achieved by providing several cutoffs under the cutoff argument. The clustering results for the different cutoffs will be stored in one data frame.
One may force the cmp.cluster function to calculate and store the distance matrix by supplying a file name to the save.distances argument. The generated distance matrix can be loaded and passed on to many other clustering methods available in R, such as the hierarchical clustering function hclust (see below).
If a distance matrix is available, it may also be supplied to cmp.cluster via the use.distances argument. This is useful when one has a pre-computed distance matrix either from a previous call to cmp.cluster or from other distance calculation subroutines.
Single-linkage binning clustering with one or multiple cutoffs:
clusters <- cmp.cluster(db=apset, cutoff = c(0.7, 0.8, 0.9), quiet = TRUE)##
## sorting result... clusters[1:12,] ## ids CLSZ_0.7 CLID_0.7 CLSZ_0.8 CLID_0.8 CLSZ_0.9 CLID_0.9
## 48 650049 2 48 2 48 2 48
## 49 650050 2 48 2 48 2 48
## 54 650059 2 54 2 54 2 54
## 55 650060 2 54 2 54 2 54
## 56 650061 2 56 2 56 2 56
## 57 650062 2 56 2 56 2 56
## 58 650063 2 58 2 58 2 58
## 59 650064 2 58 2 58 2 58
## 60 650065 2 60 2 60 2 60
## 61 650066 2 60 2 60 2 60
## 1 650001 1 1 1 1 1 1
## 2 650002 1 2 1 2 1 2Clustering of FPset objects with multiple cutoffs. This method allows to call various similarity methods provided by the fpSim function. For details consult ?fpSim.
fpset <- desc2fp(apset)
clusters2 <- cmp.cluster(fpset, cutoff=c(0.5, 0.7, 0.9), method="Tanimoto", quiet=TRUE)##
## sorting result... clusters2[1:12,] ## ids CLSZ_0.5 CLID_0.5 CLSZ_0.7 CLID_0.7 CLSZ_0.9 CLID_0.9
## 69 650074 14 11 2 69 1 69
## 79 650085 14 11 2 69 1 79
## 11 650011 14 11 1 11 1 11
## 15 650015 14 11 1 15 1 15
## 45 650046 14 11 1 45 1 45
## 47 650048 14 11 1 47 1 47
## 51 650054 14 11 1 51 1 51
## 53 650058 14 11 1 53 1 53
## 64 650069 14 11 1 64 1 64
## 65 650070 14 11 1 65 1 65
## 67 650072 14 11 1 67 1 67
## 86 650092 14 11 1 86 1 86Sames as above, but using Tversky similarity measure:
clusters3 <- cmp.cluster(fpset, cutoff=c(0.5, 0.7, 0.9),
method="Tversky", alpha=0.3, beta=0.7, quiet=TRUE) ##
## sorting result...Return cluster size distributions for each cutoff:
cluster.sizestat(clusters, cluster.result=1)## cluster size count
## 1 1 90
## 2 2 5 cluster.sizestat(clusters, cluster.result=2)## cluster size count
## 1 1 90
## 2 2 5 cluster.sizestat(clusters, cluster.result=3) ## cluster size count
## 1 1 90
## 2 2 5Enforce calculation of distance matrix:
clusters <- cmp.cluster(db=apset, cutoff = c(0.65, 0.5, 0.3),
save.distances="distmat.rda") # Saves distance matrix to file "distmat.rda" in current working directory.
load("distmat.rda") # Loads distance matrix. The Jarvis-Patrick clustering algorithm is widely used in cheminformatics (Jarvis and Patrick, 1973). It requires a nearest neighbor table, which consists of j nearest neighbors for each item (e.g. compound). The nearest neighbor table is then used to join items into clusters when they meet the following requirements: (a) they are contained in each other’s neighbor list and (b) they share at least k nearest neighbors. The values for j and k are user-defined parameters. The jarvisPatrick function implemented in ChemmineR takes a nearest neighbor table generated by nearestNeighbors, which works for APset and FPset objects. This function takes either the standard Jarvis-Patrick j parameter (as the numNbrs parameter), or else a cutoff value, which is an extension to the basic algorithm that we have added. Given a cutoff value, the nearest neighbor table returned contains every neighbor with a similarity greater than the cutoff value, for each item. This allows one to generate tighter clusters and to minimize certain limitations of this method, such as false joins of completely unrelated items when operating on small data sets. The trimNeighbors function can also be used to take an existing nearest neighbor table and remove all neighbors whose similarity value is below a given cutoff value. This allows one to compute a very relaxed nearest neighbor table initially, and then quickly try different refinements later.
In case an existing nearest neighbor matrix needs to be used, the fromNNMatrix function can be used to transform it into the list structure that jarvisPatrick requires. The input matrix must have a row for each compound, and each row should be the index values of the neighbors of compound represented by that row. The names of each compound can also be given through the names argument. If not given, it will attempt to use the rownames of the given matrix.
The jarvisPatrick function also allows one to relax some of the requirements of the algorithm through the mode parameter. When set to “a1a2b”, then all requirements are used. If set to “a1b”, then (a) is relaxed to a unidirectional requirement. Lastly, if mode is set to “b”, then only requirement (b) is used, which means that all pairs of items will be checked to see if (b) is satisfied between them. The size of the clusters generated by the different methods increases in this order: “a1a2b” < “a1b” < “b”. The run time of method “a1a2b” follows a close to linear relationship, while it is nearly quadratic for the much more exhaustive method “b”. Only methods “a1a2b” and “a1b” are suitable for clustering very large data sets (e.g. >50,000 items) in a reasonable amount of time.
An additional extension to the algorithm is the ability to set the linkage mode. The linkage parameter can be one of “single”, “average”, or “complete”, for single linkage, average linkage and complete linkage merge requirements, respectively. In the context of Jarvis-Patrick, average linkage means that at least half of the pairs between the clusters under consideration must meet requirement (b). Similarly, for complete linkage, all pairs must requirement (b). Single linkage is the normal case for Jarvis-Patrick and just means that at least one pair must meet requirement (b).
The output is a cluster vector with the item labels in the name slot and the cluster IDs in the data slot. There is a utility function called byCluster, which takes out cluster vector output by jarvisPatrick and transforms it into a list of vectors. Each slot of the list is named with a cluster id and the vector contains the cluster members. By default the function excludes singletons from the output, but they can be included by setting excludeSingletons=FALSE.
Load/create sample APset and FPset:
data(apset)
fpset <- desc2fp(apset) Standard Jarvis-Patrick clustering on APset and FPset objects:
jarvisPatrick(nearestNeighbors(apset,numNbrs=6), k=5, mode="a1a2b")## 650001 650002 650003 650004 650005 650006 650007 650008 650009 650010
## 1 2 3 4 5 6 7 8 9 10
## 650011 650012 650013 650014 650015 650016 650017 650019 650020 650021
## 11 12 13 14 11 15 16 17 18 19
## 650022 650023 650024 650025 650026 650027 650028 650029 650030 650031
## 20 21 22 23 24 25 26 27 28 29
## 650032 650033 650034 650035 650036 650037 650038 650039 650040 650041
## 30 31 32 33 34 35 36 37 38 39
## 650042 650043 650044 650045 650046 650047 650048 650049 650050 650052
## 40 41 42 43 44 45 46 47 48 49
## 650054 650056 650058 650059 650060 650061 650062 650063 650064 650065
## 50 51 52 53 54 55 56 57 58 59
## 650066 650067 650068 650069 650070 650071 650072 650073 650074 650075
## 60 61 62 63 64 65 66 67 68 69
## 650076 650077 650078 650079 650080 650081 650082 650083 650085 650086
## 70 71 72 73 74 75 76 77 78 79
## 650087 650088 650089 650090 650091 650092 650093 650094 650095 650096
## 80 81 82 83 84 85 86 87 88 89
## 650097 650098 650099 650100 650101 650102 650103 650104 650105 650106
## 90 91 92 93 94 95 96 97 98 99 #Using "APset"
jarvisPatrick(nearestNeighbors(fpset,numNbrs=6), k=5, mode="a1a2b")## 650001 650002 650003 650004 650005 650006 650007 650008 650009 650010
## 1 2 3 4 5 6 7 8 9 10
## 650011 650012 650013 650014 650015 650016 650017 650019 650020 650021
## 11 12 13 14 11 15 16 17 18 19
## 650022 650023 650024 650025 650026 650027 650028 650029 650030 650031
## 20 21 22 23 24 25 26 27 28 29
## 650032 650033 650034 650035 650036 650037 650038 650039 650040 650041
## 30 31 32 33 34 35 36 37 38 39
## 650042 650043 650044 650045 650046 650047 650048 650049 650050 650052
## 40 41 42 43 44 45 46 47 48 49
## 650054 650056 650058 650059 650060 650061 650062 650063 650064 650065
## 50 51 52 53 54 55 56 57 58 59
## 650066 650067 650068 650069 650070 650071 650072 650073 650074 650075
## 60 61 62 63 64 65 66 67 68 69
## 650076 650077 650078 650079 650080 650081 650082 650083 650085 650086
## 70 71 72 73 74 75 76 77 78 79
## 650087 650088 650089 650090 650091 650092 650093 650094 650095 650096
## 80 81 82 83 84 85 86 87 88 89
## 650097 650098 650099 650100 650101 650102 650103 650104 650105 650106
## 90 91 92 93 94 1 95 96 97 98 #Using "FPset" The following example runs Jarvis-Patrick clustering with a minimum similarity cutoff value (here Tanimoto coefficient). In addition, it uses the much more exhaustive “b” method that generates larger cluster sizes, but significantly increased the run time. For more details, consult the corresponding help file with ?jarvisPatrick.
cl<-jarvisPatrick(nearestNeighbors(fpset,cutoff=0.6,
method="Tanimoto"), k=2 ,mode="b")
byCluster(cl) ## $`11`
## [1] "650011" "650092"
##
## $`15`
## [1] "650015" "650069"
##
## $`45`
## [1] "650046" "650054"
##
## $`48`
## [1] "650049" "650050"
##
## $`52`
## [1] "650059" "650060"
##
## $`53`
## [1] "650061" "650062"
##
## $`54`
## [1] "650063" "650064"
##
## $`55`
## [1] "650065" "650066"
##
## $`62`
## [1] "650074" "650085"Output nearest neighbor table (matrix):
nnm <- nearestNeighbors(fpset,numNbrs=6)
nnm$names[1:4] ## [1] "650001" "650002" "650003" "650004" nnm$ids[1:4,] ## NULL nnm$similarities[1:4,] ## 650001 650102 650072 650015 650094 650069
## sim 1 0.4719 0.4288 0.4275 0.4247 0.4187
## sim 1 0.4344 0.4247 0.4217 0.3939 0.3922
## sim 1 0.4152 0.3619 0.3610 0.3424 0.3367
## sim 1 0.5791 0.4974 0.4193 0.4167 0.4105Trim nearest neighbor table:
nnm <- trimNeighbors(nnm,cutoff=0.4)
nnm$similarities[1:4,]## 650001 650102 650072 650015 650094 650069
## sim 1 0.4719 0.4288 0.4275 0.4247 0.4187
## sim 1 0.4344 0.4247 0.4217 NA NA
## sim 1 0.4152 NA NA NA NA
## sim 1 0.5791 0.4974 0.4193 0.4167 0.4105Perform clustering on precomputed nearest neighbor table:
jarvisPatrick(nnm, k=5,mode="b") ## 650001 650002 650003 650004 650005 650006 650007 650008 650009 650010
## 1 2 3 4 5 6 7 8 9 10
## 650011 650012 650013 650014 650015 650016 650017 650019 650020 650021
## 11 12 13 14 11 15 16 17 18 19
## 650022 650023 650024 650025 650026 650027 650028 650029 650030 650031
## 20 21 22 23 24 25 26 27 28 29
## 650032 650033 650034 650035 650036 650037 650038 650039 650040 650041
## 30 31 32 33 34 35 36 37 38 39
## 650042 650043 650044 650045 650046 650047 650048 650049 650050 650052
## 40 41 42 43 11 44 11 45 46 47
## 650054 650056 650058 650059 650060 650061 650062 650063 650064 650065
## 48 49 50 51 52 53 54 55 56 57
## 650066 650067 650068 650069 650070 650071 650072 650073 650074 650075
## 57 58 59 11 60 61 62 63 64 65
## 650076 650077 650078 650079 650080 650081 650082 650083 650085 650086
## 66 67 68 69 37 70 71 72 64 73
## 650087 650088 650089 650090 650091 650092 650093 650094 650095 650096
## 74 75 76 77 78 11 79 80 81 82
## 650097 650098 650099 650100 650101 650102 650103 650104 650105 650106
## 83 84 85 86 87 1 88 89 90 91Using a user defined nearest neighbor matrix:
nn <- matrix(c(1,2,2,1),2,2,dimnames=list(c('one','two')))
nn## [,1] [,2]
## one 1 2
## two 2 1 byCluster(jarvisPatrick(fromNNMatrix(nn),k=1)) ## $`1`
## [1] "one" "two"To visualize and compare clustering results, the cluster.visualize function can be used. The function performs Multi-Dimensional Scaling (MDS) and visualizes the results in form of a scatter plot. It requires as input an APset, a clustering result from cmp.cluster, and a cutoff for the minimum cluster size to consider in the plot. To help determining a proper cutoff size, the cluster.sizestat function is provided to generate cluster size statistics.
MDS clustering and scatter plot:
cluster.visualize(apset, clusters, size.cutoff=2, quiet = TRUE) # Color codes clusters with at least two members.
cluster.visualize(apset, clusters, quiet = TRUE) # Plots all items.Create a 3D scatter plot of MDS result:
library(scatterplot3d)
coord <- cluster.visualize(apset, clusters, size.cutoff=1, dimensions=3, quiet=TRUE)
scatterplot3d(coord) 
plot of chunk mds_scatter
Interactive 3D scatter plot with Open GL (graphics not evaluated here):
library(rgl) rgl.open(); offset <- 50;
par3d(windowRect=c(offset, offset, 640+offset, 640+offset))
rm(offset)
rgl.clear()
rgl.viewpoint(theta=45, phi=30, fov=60, zoom=1)
spheres3d(coord[,1], coord[,2], coord[,3], radius=0.03, color=coord[,4], alpha=1, shininess=20)
aspect3d(1, 1, 1)
axes3d(col='black')
title3d("", "", "", "", "", col='black')
bg3d("white") # To save a snapshot of the graph, one can use the command rgl.snapshot("test.png").ChemmineR allows the user to take advantage of the wide spectrum of clustering utilities available in R. An example on how to perform hierarchical clustering with the hclust function is given below.
Create atom pair distance matrix:
dummy <- cmp.cluster(db=apset, cutoff=0, save.distances="distmat.rda", quiet=TRUE) ##
## sorting result... load("distmat.rda") Hierarchical clustering with hclust:
hc <- hclust(as.dist(distmat), method="single")
hc[["labels"]] <- cid(apset) # Assign correct item labels
plot(as.dendrogram(hc), edgePar=list(col=4, lwd=2), horiz=T) 
plot of chunk hclust
Instead of atom pairs one can use PubChem’s fingerprints for clustering:
simMA <- sapply(cid(fpset), function(x) fpSim(fpset[x], fpset, sorted=FALSE))
hc <- hclust(as.dist(1-simMA), method="single")
plot(as.dendrogram(hc), edgePar=list(col=4, lwd=2), horiz=TRUE) Plot dendrogram with heatmap (here similarity matrix):
library(gplots) ## KernSmooth 2.23 loaded
## Copyright M. P. Wand 1997-2009
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess heatmap.2(1-distmat, Rowv=as.dendrogram(hc), Colv=as.dendrogram(hc),
col=colorpanel(40, "darkblue", "yellow", "white"),
density.info="none", trace="none") 
plot of chunk heatmap
The function getIds accepts one or more numeric PubChem compound ids and downloads the corresponding compounds from PubChem Power User Gateway (PUG) returning results in an SDFset container. The ChemMine Tools web service is used as an intermediate, to translate queries from plain HTTP POST to a PUG SOAP query.
Fetch 2 compounds from PubChem:
compounds <- getIds(c(111,123))
compounds The function searchString accepts one SMILES string (Simplified Molecular Input Line Entry Specification) and performs a >0.95 similarity PubChem fingerprint search, returning the hits in an SDFset container. The ChemMine Tools web service is used as an intermediate, to translate queries from plain HTTP POST to a PubChem Power User Gateway (PUG) query.
Search a SMILES string on PubChem:
compounds <- searchString("CC(=O)OC1=CC=CC=C1C(=O)O") compounds The function searchSim performs a PubChem similarity search just like searchString, but accepts a query in an SDFset container. If the query contains more than one compound, only the first is searched.
Search an SDFset container on PubChem:
data(sdfsample);
sdfset <- sdfsample[1]
compounds <- searchSim(sdfset)
compounds sessionInfo()R version 3.1.1 (2014-07-10) Platform: x86_64-unknown-linux-gnu (64-bit)
locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages: [1] stats graphics grDevices utils datasets methods base
other attached packages: [1] gplots_2.14.1 scatterplot3d_0.3-35 fmcsR_1.6.5
[4] RSQLite_0.11.4 DBI_0.2-7 ChemmineOB_1.2.9
[7] knitcitations_1.0-1 ChemmineR_2.16.9 knitr_1.6
loaded via a namespace (and not attached): [1] KernSmooth_2.23-12 RCurl_1.95-4.3 RJSONIO_1.3-0
[4] Rcpp_0.11.2 RefManageR_0.8.3 XML_3.98-1.1
[7] bibtex_0.3-6 bitops_1.0-6 caTools_1.17
[10] digest_0.6.4 evaluate_0.5.5 formatR_0.10
[13] gdata_2.13.3 gtools_3.4.1 httr_0.4
[16] lubridate_1.3.3 memoise_0.2.1 parallel_3.1.1
[19] plyr_1.8.1 stringr_0.6.2 tools_3.1.1
[22] zlibbioc_1.10.0
This software was developed with funding from the National Science Foundation: , 2010-0520325 and IGERT-0504249.
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