library(webchem)
library(dplyr)The lc50 dataset provided with webchem
contains acute ecotoxicity of 124 insecticides. We’ll work with a subset
of these to obtain chemical names and octanal/water partitioning
coefficients from PubChem, and gas chromatography retention indices from
the NIST Web Book.
head(lc50)
#> cas value
#> 4 50-29-3 12.415277
#> 12 52-68-6 1.282980
#> 15 55-38-9 12.168138
#> 18 56-23-5 35000.000000
#> 21 56-38-2 1.539119
#> 36 57-74-9 98.400000
lc50_sub <- lc50[1:15, ]Usually a webchem workflow starts with translating and
retrieving chemical identifiers since most chemical information
databases use their own internal identifiers.
First, we will covert CAS numbers to InChIKey identifiers using the Chemical Translation Service. Then, we’ll use these InChiKeys to get Pubchem CompoundID numbers, to use for retrieving chemical properties from PubChem.
lc50_sub$inchikey <- cts_convert(lc50_sub$cas, from = "CAS", to = "InChIKey", choices = 1, verbose = FALSE)
head(lc50_sub)
#> cas value inchikey
#> 4 50-29-3 12.415277 YVGGHNCTFXOJCH-UHFFFAOYSA-N
#> 12 52-68-6 1.282980 NFACJZMKEDPNKN-UHFFFAOYSA-N
#> 15 55-38-9 12.168138 PNVJTZOFSHSLTO-UHFFFAOYSA-N
#> 18 56-23-5 35000.000000 VZGDMQKNWNREIO-UHFFFAOYSA-N
#> 21 56-38-2 1.539119 LCCNCVORNKJIRZ-UHFFFAOYSA-N
#> 36 57-74-9 98.400000 BIWJNBZANLAXMG-YQELWRJZSA-N
any(is.na(lc50_sub$inchikey))
#> [1] FALSEGreat, now we can retrieve PubChem CIDs. All get_*()
functions return a data frame containing the query and the retrieved
identifier. We can merge this with our dataset with
dplyr::full_join()
x <- get_cid(lc50_sub$inchikey, from = "inchikey", match = "first", verbose = FALSE)
library(dplyr)
lc50_sub2 <- full_join(lc50_sub, x, by = c("inchikey" = "query"))
head(lc50_sub2)
#> cas value inchikey cid
#> 1 50-29-3 12.415277 YVGGHNCTFXOJCH-UHFFFAOYSA-N 3036
#> 2 52-68-6 1.282980 NFACJZMKEDPNKN-UHFFFAOYSA-N 5853
#> 3 55-38-9 12.168138 PNVJTZOFSHSLTO-UHFFFAOYSA-N 3346
#> 4 56-23-5 35000.000000 VZGDMQKNWNREIO-UHFFFAOYSA-N 5943
#> 5 56-38-2 1.539119 LCCNCVORNKJIRZ-UHFFFAOYSA-N 991
#> 6 57-74-9 98.400000 BIWJNBZANLAXMG-YQELWRJZSA-N 11954021Functions that query chemical information databases begin with a
prefix that matches the database. For example, functions to query
PubChem begin with pc_ and functions to query ChemSpider
begin with cs_. In this example, we’ll get the names and
log octanal/water partitioning coefficients for each compound using
PubChem, and the WHO acute toxicity rating from the PAN Pesticide
database.
y <- pc_prop(lc50_sub2$cid, properties = c("IUPACName", "XLogP"))
#> https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/property/IUPACName,XLogP/JSON
y$CID <- as.character(y$CID)
lc50_sub3 <- full_join(lc50_sub2, y, by = c("cid" = "CID"))
head(lc50_sub3)
#> cas value inchikey cid
#> 1 50-29-3 12.415277 YVGGHNCTFXOJCH-UHFFFAOYSA-N 3036
#> 2 52-68-6 1.282980 NFACJZMKEDPNKN-UHFFFAOYSA-N 5853
#> 3 55-38-9 12.168138 PNVJTZOFSHSLTO-UHFFFAOYSA-N 3346
#> 4 56-23-5 35000.000000 VZGDMQKNWNREIO-UHFFFAOYSA-N 5943
#> 5 56-38-2 1.539119 LCCNCVORNKJIRZ-UHFFFAOYSA-N 991
#> 6 57-74-9 98.400000 BIWJNBZANLAXMG-YQELWRJZSA-N 11954021
#> IUPACName XLogP
#> 1 1-chloro-4-[2,2,2-trichloro-1-(4-chlorophenyl)ethyl]benzene 6.9
#> 2 2,2,2-trichloro-1-dimethoxyphosphorylethanol 0.5
#> 3 dimethoxy-(3-methyl-4-methylsulfanylphenoxy)-sulfanylidene-lambda5-phosphane 4.1
#> 4 tetrachloromethane 2.8
#> 5 diethoxy-(4-nitrophenoxy)-sulfanylidene-lambda5-phosphane 3.8
#> 6 (1R,7S)-1,3,4,7,8,9,10,10-octachlorotricyclo[5.2.1.02,6]dec-8-ene 4.9The IUPAC names are long and unwieldy, and one could use
pc_synonyms() to choose better names. Several other
functions return synonyms as well, even though they are not explicitly
translator type functions. We’ll see an example of that next.
Many of the chemical databases webchem can query contain
vast amounts of information in a variety of structures. Therefore, some
webchem functions return nested lists rather than data
frames. pan_query() is one such function.
out <- pan_query(lc50_sub3$cas, verbose = FALSE)
#> Warning in lapply(out[tonum], as.numeric): NAs introduced by coercion
#> Warning in lapply(out[tonum], as.numeric): NAs introduced by coercion
#> Warning in lapply(out[tonum], as.numeric): NAs introduced by coercion
#> Warning in lapply(out[tonum], as.numeric): NAs introduced by coercion
#> Warning in lapply(out[tonum], as.numeric): NAs introduced by coercion
#> Warning in lapply(out[tonum], as.numeric): NAs introduced by coercion
#> Warning in lapply(out[tonum], as.numeric): NAs introduced by coercionout is a nested list which you can inspect with
View(). It has an element for each query, and within each
query, many elements corresponding to different properties in the
database. To extract a single property from all queries, we need to use
a mapping function such as sapply() or one of the
map_*() functions from the purrr package.
lc50_sub3$who_tox <- sapply(out, function(y) y$`WHO Acute Toxicity`)
lc50_sub3$common_name <- sapply(out, function(y) y$`Chemical name`)
# #equivalent with purrr package:
# lc50_sub3$who_tox <- map_chr(out, pluck, "WHO Acute Toxicity")
# lc50_sub3$common_name <- map_chr(out, pluck, "Chemical name")#tidy up columns
lc50_done <- dplyr::select(lc50_sub3, common_name, cas, inchikey, XLogP, who_tox)
head(lc50_done)
#> common_name cas inchikey XLogP who_tox
#> 1 DDT, p,p' 50-29-3 YVGGHNCTFXOJCH-UHFFFAOYSA-N 6.9 II, Moderately Hazardous
#> 2 Trichlorfon 52-68-6 NFACJZMKEDPNKN-UHFFFAOYSA-N 0.5 II, Moderately Hazardous
#> 3 Fenthion 55-38-9 PNVJTZOFSHSLTO-UHFFFAOYSA-N 4.1 II, Moderately Hazardous
#> 4 Carbon tetrachloride 56-23-5 VZGDMQKNWNREIO-UHFFFAOYSA-N 2.8 Not Listed
#> 5 Parathion 56-38-2 LCCNCVORNKJIRZ-UHFFFAOYSA-N 3.8 Ia, Extremely Hazardous
#> 6 Chlordane 57-74-9 BIWJNBZANLAXMG-YQELWRJZSA-N 4.9 II, Moderately Hazardous