CAS Common Chemistry API in R#
by Adam M. Nguyen and Michael T. Moen
The CAS Common Chemistry API provides programmatic access to a curated subset, offering information on nearly 500,000 chemical substances.
Please see the following resources for more information on API usage:
Documentation
Terms
Data Reuse
CAS Common Chemistry is provided under the Creative Commons CC BY-NC 4.0 license
NOTE: Please see access details and rate limit requests for this API in the official documentation.
These recipe examples were tested on March 24, 2026.
Setup#
Load Libraries#
The following packages need to be installed into your environment to run the code examples in this tutorial. These packages can be installed with install.packages().
We load the libraries used in this tutorial below:
library(httr)
library(jsonlite)
library(magick)
Import API Key#
An API key is required to access the CAS Common Chemistry API. You can sign up for one at the Request API Access for CAS Common Chemistry page.
We keep our token in a .Renviron file that is stored in the working directory and use Sys.getenv() to access it. The .Renviron should have an entry like the one below.
CAS_API_KEY="PUT_YOUR_API_KEY_HERE"
Below, we can test to whether the key was successfully imported.
if (nzchar(Sys.getenv("CAS_API_KEY"))) {
print("API key successfully loaded.")
} else {
warning("API key not found or is empty.")
}
## [1] "API key successfully loaded."
1. Common Chemistry Record Detail Retrieval#
Common Chemistry is an extremely useful open community resource for accessing chemical information for nearly 500,000 chemical chemistry from the CAS registry. In this example, we detail the steps for accessing a simple example chemical substance, ethyl cyclohexanepropionate.
API Parameter Setup#
First, let’s setup the API parameters for Common Chemistry. Below we are going to provide a variable detail_base_url to query Common Chemistry for details and more specifically. In this example, using a registry number, or as we abbreviate it an rn.
DETAIL_BASE_URL <- "https://commonchemistry.cas.org/api/detail?"
casrn1 <- "10094-36-7" # ethyl cyclohexanepropionate
Request Data from CAS Common Chemistry Detail API#
Using the httr and jsonlite libraries, we query the API and convert the data to a usable format.
url <- paste0(DETAIL_BASE_URL, "cas_rn=", casrn1)
# Use httr's 'GET()' function to retrieve raw data from a URL
casrn1_response <-GET(url, add_headers("X-API-KEY" = Sys.getenv("CAS_API_KEY")))
casrn1_response
## Response [https://commonchemistry.cas.org/api/detail?cas_rn=10094-36-7]
## Date: 2026-03-24 18:05
## Status: 200
## Content-Type: application/json
## Size: 5.31 kB
The GET() function returns Response, Date, Status: 200 indicating a successful request, Size, and Content-Type. In most cases when accessing APIs, the Content-Type returns JSON, so we need the jsonlite to parse it.
# Extract JSON data from the response
casrn1_data <- fromJSON(rawToChar(casrn1_response$content))
casrn1_data
## $uri
## [1] "substance/pt/10094367"
##
## $rn
## [1] "10094-36-7"
##
## $name
## [1] "Ethyl cyclohexanepropionate"
##
## $images
## [1] "<svg width=\"215\" viewBox=\"0 0 215 101\" style=\"fill-opacity:1; color-rendering:auto; color-interpolation:auto; text-rendering:auto; stroke:black; stroke-linecap:square; stroke-miterlimit:10; shape-rendering:auto; stroke-opacity:1; fill:black; stroke-dasharray:none; font-weight:normal; stroke-width:1; font-family:'Open Sans'; font-style:normal; stroke-linejoin:miter; font-size:12; stroke-dashoffset:0; image-rendering:auto;\" height=\"101\" class=\"cas-substance-image\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns=\"http://www.w3.org/2000/svg\"><svg class=\"cas-substance-single-component\"><rect y=\"0\" x=\"0\" width=\"215\" stroke=\"none\" ry=\"7\" rx=\"7\" height=\"101\" fill=\"white\" class=\"cas-substance-group\"/><svg y=\"0\" x=\"0\" width=\"215\" viewBox=\"0 0 215 101\" style=\"fill:black;\" height=\"101\" class=\"cas-substance-single-component-image\"><svg><g><g transform=\"translate(107,49)\" style=\"text-rendering:geometricPrecision; color-rendering:optimizeQuality; color-interpolation:linearRGB; stroke-linecap:butt; image-rendering:optimizeQuality;\"><line y2=\"0\" y1=\"15\" x2=\"0\" x1=\"-25.98\" style=\"fill:none;\"/><line y2=\"0\" y1=\"15\" x2=\"-51.963\" x1=\"-25.98\" style=\"fill:none;\"/><line y2=\"15\" y1=\"0\" x2=\"25.98\" x1=\"0\" style=\"fill:none;\"/><line y2=\"3.1886\" y1=\"15\" x2=\"46.4398\" x1=\"25.98\" style=\"fill:none;\"/><line y2=\"38.5234\" y1=\"13.9896\" x2=\"24.23\" x1=\"24.23\" style=\"fill:none;\"/><line y2=\"38.5234\" y1=\"13.9897\" x2=\"27.73\" x1=\"27.73\" style=\"fill:none;\"/><line y2=\"15\" y1=\"3.1786\" x2=\"77.943\" x1=\"57.4684\" style=\"fill:none;\"/><line y2=\"0\" y1=\"15\" x2=\"103.923\" x1=\"77.943\" style=\"fill:none;\"/><line y2=\"15\" y1=\"0\" x2=\"-77.943\" x1=\"-51.963\" style=\"fill:none;\"/><line y2=\"-30\" y1=\"0\" x2=\"-51.963\" x1=\"-51.963\" style=\"fill:none;\"/><line y2=\"0\" y1=\"15\" x2=\"-103.923\" x1=\"-77.943\" style=\"fill:none;\"/><line y2=\"-45\" y1=\"-30\" x2=\"-77.943\" x1=\"-51.963\" style=\"fill:none;\"/><line y2=\"-30\" y1=\"0\" x2=\"-103.923\" x1=\"-103.923\" style=\"fill:none;\"/><line y2=\"-30\" y1=\"-45\" x2=\"-103.923\" x1=\"-77.943\" style=\"fill:none;\"/><path style=\"fill:none; stroke-miterlimit:5;\" d=\"M-25.547 14.75 L-25.98 15 L-26.413 14.75\"/><path style=\"fill:none; stroke-miterlimit:5;\" d=\"M-0.433 0.25 L0 0 L0.433 0.25\"/><path style=\"fill:none; stroke-miterlimit:5;\" d=\"M25.547 14.75 L25.98 15 L26.413 14.75\"/></g><g transform=\"translate(107,49)\" style=\"stroke-linecap:butt; fill:rgb(230,0,0); text-rendering:geometricPrecision; color-rendering:optimizeQuality; image-rendering:optimizeQuality; font-family:'Open Sans'; stroke:rgb(230,0,0); color-interpolation:linearRGB; stroke-miterlimit:5;\"><path style=\"stroke:none;\" d=\"M55.9005 -0.0703 Q55.9005 1.9922 54.8614 3.1719 Q53.8224 4.3516 51.9786 4.3516 Q50.088 4.3516 49.0568 3.1875 Q48.0255 2.0234 48.0255 -0.0859 Q48.0255 -2.1797 49.0568 -3.3281 Q50.088 -4.4766 51.9786 -4.4766 Q53.838 -4.4766 54.8693 -3.3047 Q55.9005 -2.1328 55.9005 -0.0703 ZM49.0724 -0.0703 Q49.0724 1.6641 49.8146 2.5703 Q50.5568 3.4766 51.9786 3.4766 Q53.4005 3.4766 54.1271 2.5781 Q54.8536 1.6797 54.8536 -0.0703 Q54.8536 -1.8047 54.1271 -2.6953 Q53.4005 -3.5859 51.9786 -3.5859 Q50.5568 -3.5859 49.8146 -2.6875 Q49.0724 -1.7891 49.0724 -0.0703 Z\"/><path style=\"stroke:none;\" d=\"M29.9175 44.9297 Q29.9175 46.9922 28.8784 48.1719 Q27.8394 49.3516 25.9956 49.3516 Q24.105 49.3516 23.0737 48.1875 Q22.0425 47.0234 22.0425 44.9141 Q22.0425 42.8203 23.0737 41.6719 Q24.105 40.5234 25.9956 40.5234 Q27.855 40.5234 28.8862 41.6953 Q29.9175 42.8672 29.9175 44.9297 ZM23.0894 44.9297 Q23.0894 46.6641 23.8316 47.5703 Q24.5737 48.4766 25.9956 48.4766 Q27.4175 48.4766 28.1441 47.5781 Q28.8706 46.6797 28.8706 44.9297 Q28.8706 43.1953 28.1441 42.3047 Q27.4175 41.4141 25.9956 41.4141 Q24.5737 41.4141 23.8316 42.3125 Q23.0894 43.2109 23.0894 44.9297 Z\"/><path style=\"fill:none; stroke:black;\" d=\"M77.51 14.75 L77.943 15 L78.376 14.75\"/><path style=\"fill:none; stroke:black;\" d=\"M-77.51 14.75 L-77.943 15 L-78.376 14.75\"/><path style=\"fill:none; stroke:black;\" d=\"M-51.963 -29.5 L-51.963 -30 L-52.396 -30.25\"/><path style=\"fill:none; stroke:black;\" d=\"M-103.49 0.25 L-103.923 0 L-103.923 -0.5\"/><path style=\"fill:none; stroke:black;\" d=\"M-77.51 -44.75 L-77.943 -45 L-78.376 -44.75\"/><path style=\"fill:none; stroke:black;\" d=\"M-103.923 -29.5 L-103.923 -30 L-103.49 -30.25\"/></g></g></svg></svg></svg></svg>"
##
## $inchi
## [1] "InChI=1S/C11H20O2/c1-2-13-11(12)9-8-10-6-4-3-5-7-10/h10H,2-9H2,1H3"
##
## $inchiKey
## [1] "InChIKey=NRVPMFHPHGBQLP-UHFFFAOYSA-N"
##
## $smile
## [1] ""
##
## $canonicalSmile
## [1] "O=C(OCC)CCC1CCCCC1"
##
## $molecularFormula
## [1] "C<sub>11</sub>H<sub>20</sub>O<sub>2</sub>"
##
## $molecularMass
## [1] "184.28"
##
## $experimentalProperties
## name property sourceNumber
## 1 Boiling Point 105-113 °C 0
##
## $propertyCitations
## list()
##
## $synonyms
## [1] "Cyclohexanepropanoic acid, ethyl ester"
## [2] "Cyclohexanepropionic acid, ethyl ester"
## [3] "Ethyl cyclohexanepropionate"
## [4] "Ethyl cyclohexylpropanoate"
## [5] "Ethyl 3-cyclohexylpropionate"
## [6] "Ethyl 3-cyclohexylpropanoate"
## [7] "3-Cyclohexylpropionic acid ethyl ester"
## [8] "NSC 71463"
## [9] "Ethyl 3-cyclohexanepropionate"
##
## $replacedRns
## list()
##
## $hasMolfile
## [1] TRUE
Accessing Specific Data#
The $ operator can be used to extract specific parts of casrn1_data.
# Access the canonical SMILE
casrn1_smile <- casrn1_data$canonicalSmile
casrn1_smile
## [1] "O=C(OCC)CCC1CCCCC1"
# Another example may be finding synonyms for Ethyl cyclohexanepropionate
casrn1_synonyms <- casrn1_data$synonyms
casrn1_synonyms
## [1] "Cyclohexanepropanoic acid, ethyl ester"
## [2] "Cyclohexanepropionic acid, ethyl ester"
## [3] "Ethyl cyclohexanepropionate"
## [4] "Ethyl cyclohexylpropanoate"
## [5] "Ethyl 3-cyclohexylpropionate"
## [6] "Ethyl 3-cyclohexylpropanoate"
## [7] "3-Cyclohexylpropionic acid ethyl ester"
## [8] "NSC 71463"
## [9] "Ethyl 3-cyclohexanepropionate"
The methods above work for any parts of the data object, to find a list of the data objects, you can use the ls() function.
ls(casrn1_data)
## [1] "canonicalSmile" "experimentalProperties" "hasMolfile"
## [4] "images" "inchi" "inchiKey"
## [7] "molecularFormula" "molecularMass" "name"
## [10] "propertyCitations" "replacedRns" "rn"
## [13] "smile" "synonyms" "uri"
Given this list we can select different data as seen in previous examples, i.e. casrn1_data$(example).
Display the Molecule Drawing#
The images section of the data contains data formatted as Scalable Vector Graphics (SVG). SVG is an XML based vector format for defining two-dimensional graphics and has been an open standard since 1999. Using the SVG data provided by the API, we display the molecule drawing using the package magick, a useful toolkit for image processing in R.
image_str <- charToRaw(casrn1_data$image)
image_read(image_str, density = 300)
2. Common Chemistry API Record Detail Retrieval in a Loop#
In this example, we show how to query Common Chemistry for details given a list of registry numbers.
# Create a list of CAS RNs
casrns <- c(
"10094-36-7",
"10031-92-2",
"10199-61-8",
"10036-21-2",
"1019020-13-3"
)
# Iterate through list of CAS RN and append to list
list <- lapply(casrns, function(casrn) {
url <- paste0(DETAIL_BASE_URL, "cas_rn=", casrn)
response <- GET(url, add_headers("X-API-KEY" = Sys.getenv("CAS_API_KEY")))
casrn_data <- fromJSON(rawToChar(response$content))
Sys.sleep(1)
return(casrn_data)
})
# Show first result in the list
head(list, n = 1)
## [[1]]
## [[1]]$uri
## [1] "substance/pt/10094367"
##
## [[1]]$rn
## [1] "10094-36-7"
##
## [[1]]$name
## [1] "Ethyl cyclohexanepropionate"
##
## [[1]]$images
## [1] "<svg width=\"215\" viewBox=\"0 0 215 101\" style=\"fill-opacity:1; color-rendering:auto; color-interpolation:auto; text-rendering:auto; stroke:black; stroke-linecap:square; stroke-miterlimit:10; shape-rendering:auto; stroke-opacity:1; fill:black; stroke-dasharray:none; font-weight:normal; stroke-width:1; font-family:'Open Sans'; font-style:normal; stroke-linejoin:miter; font-size:12; stroke-dashoffset:0; image-rendering:auto;\" height=\"101\" class=\"cas-substance-image\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns=\"http://www.w3.org/2000/svg\"><svg class=\"cas-substance-single-component\"><rect y=\"0\" x=\"0\" width=\"215\" stroke=\"none\" ry=\"7\" rx=\"7\" height=\"101\" fill=\"white\" class=\"cas-substance-group\"/><svg y=\"0\" x=\"0\" width=\"215\" viewBox=\"0 0 215 101\" style=\"fill:black;\" height=\"101\" class=\"cas-substance-single-component-image\"><svg><g><g transform=\"translate(107,49)\" style=\"text-rendering:geometricPrecision; color-rendering:optimizeQuality; color-interpolation:linearRGB; stroke-linecap:butt; image-rendering:optimizeQuality;\"><line y2=\"0\" y1=\"15\" x2=\"0\" x1=\"-25.98\" style=\"fill:none;\"/><line y2=\"0\" y1=\"15\" x2=\"-51.963\" x1=\"-25.98\" style=\"fill:none;\"/><line y2=\"15\" y1=\"0\" x2=\"25.98\" x1=\"0\" style=\"fill:none;\"/><line y2=\"3.1886\" y1=\"15\" x2=\"46.4398\" x1=\"25.98\" style=\"fill:none;\"/><line y2=\"38.5234\" y1=\"13.9896\" x2=\"24.23\" x1=\"24.23\" style=\"fill:none;\"/><line y2=\"38.5234\" y1=\"13.9897\" x2=\"27.73\" x1=\"27.73\" style=\"fill:none;\"/><line y2=\"15\" y1=\"3.1786\" x2=\"77.943\" x1=\"57.4684\" style=\"fill:none;\"/><line y2=\"0\" y1=\"15\" x2=\"103.923\" x1=\"77.943\" style=\"fill:none;\"/><line y2=\"15\" y1=\"0\" x2=\"-77.943\" x1=\"-51.963\" style=\"fill:none;\"/><line y2=\"-30\" y1=\"0\" x2=\"-51.963\" x1=\"-51.963\" style=\"fill:none;\"/><line y2=\"0\" y1=\"15\" x2=\"-103.923\" x1=\"-77.943\" style=\"fill:none;\"/><line y2=\"-45\" y1=\"-30\" x2=\"-77.943\" x1=\"-51.963\" style=\"fill:none;\"/><line y2=\"-30\" y1=\"0\" x2=\"-103.923\" x1=\"-103.923\" style=\"fill:none;\"/><line y2=\"-30\" y1=\"-45\" x2=\"-103.923\" x1=\"-77.943\" style=\"fill:none;\"/><path style=\"fill:none; stroke-miterlimit:5;\" d=\"M-25.547 14.75 L-25.98 15 L-26.413 14.75\"/><path style=\"fill:none; stroke-miterlimit:5;\" d=\"M-0.433 0.25 L0 0 L0.433 0.25\"/><path style=\"fill:none; stroke-miterlimit:5;\" d=\"M25.547 14.75 L25.98 15 L26.413 14.75\"/></g><g transform=\"translate(107,49)\" style=\"stroke-linecap:butt; fill:rgb(230,0,0); text-rendering:geometricPrecision; color-rendering:optimizeQuality; image-rendering:optimizeQuality; font-family:'Open Sans'; stroke:rgb(230,0,0); color-interpolation:linearRGB; stroke-miterlimit:5;\"><path style=\"stroke:none;\" d=\"M55.9005 -0.0703 Q55.9005 1.9922 54.8614 3.1719 Q53.8224 4.3516 51.9786 4.3516 Q50.088 4.3516 49.0568 3.1875 Q48.0255 2.0234 48.0255 -0.0859 Q48.0255 -2.1797 49.0568 -3.3281 Q50.088 -4.4766 51.9786 -4.4766 Q53.838 -4.4766 54.8693 -3.3047 Q55.9005 -2.1328 55.9005 -0.0703 ZM49.0724 -0.0703 Q49.0724 1.6641 49.8146 2.5703 Q50.5568 3.4766 51.9786 3.4766 Q53.4005 3.4766 54.1271 2.5781 Q54.8536 1.6797 54.8536 -0.0703 Q54.8536 -1.8047 54.1271 -2.6953 Q53.4005 -3.5859 51.9786 -3.5859 Q50.5568 -3.5859 49.8146 -2.6875 Q49.0724 -1.7891 49.0724 -0.0703 Z\"/><path style=\"stroke:none;\" d=\"M29.9175 44.9297 Q29.9175 46.9922 28.8784 48.1719 Q27.8394 49.3516 25.9956 49.3516 Q24.105 49.3516 23.0737 48.1875 Q22.0425 47.0234 22.0425 44.9141 Q22.0425 42.8203 23.0737 41.6719 Q24.105 40.5234 25.9956 40.5234 Q27.855 40.5234 28.8862 41.6953 Q29.9175 42.8672 29.9175 44.9297 ZM23.0894 44.9297 Q23.0894 46.6641 23.8316 47.5703 Q24.5737 48.4766 25.9956 48.4766 Q27.4175 48.4766 28.1441 47.5781 Q28.8706 46.6797 28.8706 44.9297 Q28.8706 43.1953 28.1441 42.3047 Q27.4175 41.4141 25.9956 41.4141 Q24.5737 41.4141 23.8316 42.3125 Q23.0894 43.2109 23.0894 44.9297 Z\"/><path style=\"fill:none; stroke:black;\" d=\"M77.51 14.75 L77.943 15 L78.376 14.75\"/><path style=\"fill:none; stroke:black;\" d=\"M-77.51 14.75 L-77.943 15 L-78.376 14.75\"/><path style=\"fill:none; stroke:black;\" d=\"M-51.963 -29.5 L-51.963 -30 L-52.396 -30.25\"/><path style=\"fill:none; stroke:black;\" d=\"M-103.49 0.25 L-103.923 0 L-103.923 -0.5\"/><path style=\"fill:none; stroke:black;\" d=\"M-77.51 -44.75 L-77.943 -45 L-78.376 -44.75\"/><path style=\"fill:none; stroke:black;\" d=\"M-103.923 -29.5 L-103.923 -30 L-103.49 -30.25\"/></g></g></svg></svg></svg></svg>"
##
## [[1]]$inchi
## [1] "InChI=1S/C11H20O2/c1-2-13-11(12)9-8-10-6-4-3-5-7-10/h10H,2-9H2,1H3"
##
## [[1]]$inchiKey
## [1] "InChIKey=NRVPMFHPHGBQLP-UHFFFAOYSA-N"
##
## [[1]]$smile
## [1] ""
##
## [[1]]$canonicalSmile
## [1] "O=C(OCC)CCC1CCCCC1"
##
## [[1]]$molecularFormula
## [1] "C<sub>11</sub>H<sub>20</sub>O<sub>2</sub>"
##
## [[1]]$molecularMass
## [1] "184.28"
##
## [[1]]$experimentalProperties
## name property sourceNumber
## 1 Boiling Point 105-113 °C 0
##
## [[1]]$propertyCitations
## list()
##
## [[1]]$synonyms
## [1] "Cyclohexanepropanoic acid, ethyl ester"
## [2] "Cyclohexanepropionic acid, ethyl ester"
## [3] "Ethyl cyclohexanepropionate"
## [4] "Ethyl cyclohexylpropanoate"
## [5] "Ethyl 3-cyclohexylpropionate"
## [6] "Ethyl 3-cyclohexylpropanoate"
## [7] "3-Cyclohexylpropionic acid ethyl ester"
## [8] "NSC 71463"
## [9] "Ethyl 3-cyclohexanepropionate"
##
## [[1]]$replacedRns
## list()
##
## [[1]]$hasMolfile
## [1] TRUE
We now have a list of sub-lists of data corresponding to the CAS RNs.
Display Images#
# Use 'lapply' to iterate through list and convert our image data to a readable format
imgs <- lapply(list, function(x) {charToRaw(x$image)})
# Use 'image_read()' to read SVGs
# "Ethyl cyclohexanepropionate"
image_read(imgs[[1]], density = 300)
# "Ethyl 2-nonynoate"
image_read(imgs[[2]], density = 300)
# "Ethyl 1<em>H</em>-pyrazole-1-acetate"
image_read(imgs[[3]], density = 300)
# "Ethyl 3-(ethoxycarbonyl)benzenepropanoate"
image_read(imgs[[4]], density = 300)
# "Ethyl 1-cyclohexene-1-carboximidate"
image_read(imgs[[5]], density = 300)
Select Specific Data#
We use lapply() to iterate through the list and grab each sub-list’s canonicalSmile.
# Apply function(i) to receive canonicalSmiles for every instance in the list
cansmiles <- lapply(list, function(i) {i$canonicalSmile})
unlist(cansmiles) # Flatten list
## [1] "O=C(OCC)CCC1CCCCC1" "O=C(C#CCCCCCC)OCC"
## [3] "O=C(OCC)CN1N=CC=C1" "O=C(OCC)C1=CC=CC(=C1)CCC(=O)OCC"
## [5] "N=C(OCC)C1=CCCCC1"
Through similar means, we can also iterate through the list and grab each sub-list’s synonyms.
synonyms_list <- lapply(list, function(j) {j$synonyms})
synonyms_list
## [[1]]
## [1] "Cyclohexanepropanoic acid, ethyl ester"
## [2] "Cyclohexanepropionic acid, ethyl ester"
## [3] "Ethyl cyclohexanepropionate"
## [4] "Ethyl cyclohexylpropanoate"
## [5] "Ethyl 3-cyclohexylpropionate"
## [6] "Ethyl 3-cyclohexylpropanoate"
## [7] "3-Cyclohexylpropionic acid ethyl ester"
## [8] "NSC 71463"
## [9] "Ethyl 3-cyclohexanepropionate"
##
## [[2]]
## [1] "2-Nonynoic acid, ethyl ester" "Ethyl 2-nonynoate"
## [3] "NSC 190985"
##
## [[3]]
## [1] "1<em>H</em>-Pyrazole-1-acetic acid, ethyl ester"
## [2] "Pyrazole-1-acetic acid, ethyl ester"
## [3] "Ethyl 1<em>H</em>-pyrazole-1-acetate"
## [4] "Ethyl 1-pyrazoleacetate"
## [5] "Ethyl 2-(1<em>H</em>-pyrazol-1-yl)acetate"
##
## [[4]]
## [1] "Benzenepropanoic acid, 3-(ethoxycarbonyl)-, ethyl ester"
## [2] "Hydrocinnamic acid, <em>m</em>-carboxy-, diethyl ester"
## [3] "Ethyl 3-(ethoxycarbonyl)benzenepropanoate"
##
## [[5]]
## [1] "1-Cyclohexene-1-carboximidic acid, ethyl ester"
## [2] "Ethyl 1-cyclohexene-1-carboximidate"
# Use the unlist() function to flatten our list of synonyms
flatsyns <- unlist(synonyms_list)
flatsyns
## [1] "Cyclohexanepropanoic acid, ethyl ester"
## [2] "Cyclohexanepropionic acid, ethyl ester"
## [3] "Ethyl cyclohexanepropionate"
## [4] "Ethyl cyclohexylpropanoate"
## [5] "Ethyl 3-cyclohexylpropionate"
## [6] "Ethyl 3-cyclohexylpropanoate"
## [7] "3-Cyclohexylpropionic acid ethyl ester"
## [8] "NSC 71463"
## [9] "Ethyl 3-cyclohexanepropionate"
## [10] "2-Nonynoic acid, ethyl ester"
## [11] "Ethyl 2-nonynoate"
## [12] "NSC 190985"
## [13] "1<em>H</em>-Pyrazole-1-acetic acid, ethyl ester"
## [14] "Pyrazole-1-acetic acid, ethyl ester"
## [15] "Ethyl 1<em>H</em>-pyrazole-1-acetate"
## [16] "Ethyl 1-pyrazoleacetate"
## [17] "Ethyl 2-(1<em>H</em>-pyrazol-1-yl)acetate"
## [18] "Benzenepropanoic acid, 3-(ethoxycarbonyl)-, ethyl ester"
## [19] "Hydrocinnamic acid, <em>m</em>-carboxy-, diethyl ester"
## [20] "Ethyl 3-(ethoxycarbonyl)benzenepropanoate"
## [21] "1-Cyclohexene-1-carboximidic acid, ethyl ester"
## [22] "Ethyl 1-cyclohexene-1-carboximidate"
Create Data Frame#
Let’s create a data frame from list with the variables uri, rn, name, inchiKey, canonicalSmile, and molecularMass.
df <- data.frame(do.call
# Iterate through list of chemicals with function(i) and append to df
(rbind, lapply(list, function(item)
cbind(
item["uri"],
item["rn"],
item["name"],
item["inchiKey"],
item["canonicalSmile"],
item["molecularMass"]
))),
row.names = NULL)
names(df) <- c("uri", "rn", "name", "inchiKey", "canonicalSmile", "molecularMass")
# Display df
df
## uri rn
## 1 substance/pt/10094367 10094-36-7
## 2 substance/pt/10031922 10031-92-2
## 3 substance/pt/10199618 10199-61-8
## 4 substance/pt/10036212 10036-21-2
## 5 substance/pt/1019020133 1019020-13-3
## name
## 1 Ethyl cyclohexanepropionate
## 2 Ethyl 2-nonynoate
## 3 Ethyl 1<em>H</em>-pyrazole-1-acetate
## 4 Ethyl 3-(ethoxycarbonyl)benzenepropanoate
## 5 Ethyl 1-cyclohexene-1-carboximidate
## inchiKey canonicalSmile
## 1 InChIKey=NRVPMFHPHGBQLP-UHFFFAOYSA-N O=C(OCC)CCC1CCCCC1
## 2 InChIKey=BFZNMUGAZYAMTG-UHFFFAOYSA-N O=C(C#CCCCCCC)OCC
## 3 InChIKey=SEHJVNBWAGPXSM-UHFFFAOYSA-N O=C(OCC)CN1N=CC=C1
## 4 InChIKey=HDGSRVUQMHJTDH-UHFFFAOYSA-N O=C(OCC)C1=CC=CC(=C1)CCC(=O)OCC
## 5 InChIKey=OYGZRJWLVOOMBM-UHFFFAOYSA-N N=C(OCC)C1=CCCCC1
## molecularMass
## 1 184.28
## 2 182.26
## 3 154.17
## 4 250.29
## 5 153.22
3. Common Chemistry#
In addition to the /detail endpoint, the CAS Common Chemistry API has a /search endpoint that allows searching by CAS RN, SMILES, InChI/InChIKey, and name.
Request data form CAS Common Chemistry Search API#
SEARCH_BASE_URL <- "https://commonchemistry.cas.org/api/search?q="
# InChIKey for Quinine
IK <- "InChIKey=LOUPRKONTZGTKE-WZBLMQSHSA-N"
url <- paste0(SEARCH_BASE_URL, IK)
search_response <- GET(url, add_headers("X-API-KEY" = Sys.getenv("CAS_API_KEY")))
search_response
## Response [https://commonchemistry.cas.org/api/search?q=InChIKey=LOUPRKONTZGTKE-WZBLMQSHSA-N]
## Date: 2026-03-24 18:05
## Status: 200
## Content-Type: application/json
## Size: 17.5 kB
Like before, we need to access the content part of the data to retrieve what we are interested. This data is stored in JSON.
# Extract JSON data from response
quinine_rn <- fromJSON(rawToChar(search_response$content))
quinine_rn
## $count
## [1] 1
##
## $results
## rn name
## 1 130-95-0 Quinine
## images
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Now we have the quinine’s CAS RN, so we can query quinine based on this, similar to the first example.
# Get detailed record for quinine
url <- paste0(DETAIL_BASE_URL, "cas_rn=", quinine_rn$results$rn)
detail_response <- GET(url, add_headers("X-API-KEY" = Sys.getenv("CAS_API_KEY")))
detail_response
## Response [https://commonchemistry.cas.org/api/detail?cas_rn=130-95-0]
## Date: 2026-03-24 18:05
## Status: 200
## Content-Type: application/json
## Size: 19.1 kB
# Convert JSON structure to character format
quinine_data <- fromJSON(rawToChar(detail_response$content))
quinine_data
## $uri
## [1] "substance/pt/130950"
##
## $rn
## [1] "130-95-0"
##
## $name
## [1] "Quinine"
##
## $images
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##
## $inchi
## [1] "InChI=1S/C20H24N2O2/c1-3-13-12-22-9-7-14(13)10-19(22)20(23)16-6-8-21-18-5-4-15(24-2)11-17(16)18/h3-6,8,11,13-14,19-20,23H,1,7,9-10,12H2,2H3/t13-,14-,19-,20+/m0/s1"
##
## $inchiKey
## [1] "InChIKey=LOUPRKONTZGTKE-WZBLMQSHSA-N"
##
## $smile
## [1] "[C@@H](O)(C=1C2=C(C=CC(OC)=C2)N=CC1)[C@]3([N@@]4C[C@H](C=C)[C@](C3)(CC4)[H])[H]"
##
## $canonicalSmile
## [1] "OC(C=1C=CN=C2C=CC(OC)=CC21)C3N4CCC(C3)C(C=C)C4"
##
## $molecularFormula
## [1] "C<sub>20</sub>H<sub>24</sub>N<sub>2</sub>O<sub>2</sub>"
##
## $molecularMass
## [1] "324.42"
##
## $experimentalProperties
## name property sourceNumber
## 1 Melting Point 177 °C (decomp) 0
##
## $propertyCitations
## list()
##
## $synonyms
## [1] "Cinchonan-9-ol, 6′-methoxy-, (8α,9<em>R</em>)-"
## [2] "Quinine"
## [3] "(8α,9<em>R</em>)-6′-Methoxycinchonan-9-ol"
## [4] "6′-Methoxycinchonidine"
## [5] "(-)-Quinine"
## [6] "(8<em>S</em>,9<em>R</em>)-Quinine"
## [7] "(<em>R</em>)-(-)-Quinine"
## [8] "NSC 192949"
## [9] "WR297608"
## [10] "Qualaquin"
## [11] "Mosgard"
## [12] "Quinlup"
## [13] "Quine 9"
## [14] "Cinkona"
## [15] "Quinex"
## [16] "Quinlex"
## [17] "Rezquin"
## [18] "QSM"
## [19] "SW 85833"
## [20] "(<em>R</em>)-(6-Methoxy-4-quinolyl)[(2<em>S</em>)-5-vinylquinuclidin-2-yl]methanol"
## [21] "MeSH ID: D011803"
##
## $replacedRns
## [1] "6912-57-8" "12239-42-8" "21480-31-9" "55980-20-6" "72646-90-3"
## [6] "95650-40-1" "128544-03-6" "767303-40-2" "840482-04-4" "857212-53-4"
## [11] "864908-93-0" "875538-34-4" "888714-03-2" "890027-24-4" "894767-09-0"
## [16] "898813-59-7" "898814-28-3" "899813-83-3" "900786-66-5" "900789-95-9"
## [21] "906550-97-8" "909263-47-4" "909767-48-2" "909882-78-6" "910878-25-0"
## [26] "910880-97-6" "911445-75-5" "918778-04-8" "1071756-51-8" "1267651-57-9"
## [31] "1628705-47-4" "2244812-93-7" "2244812-97-1" "2409557-51-1" "2566761-34-8"
## [36] "2929261-86-7" "3032276-91-5" "3041057-60-4" "3054777-52-2"
##
## $hasMolfile
## [1] TRUE
Handle Multiple Results#
# SMILES for butadiene
smi_bd <- "C=CC=C"
# Request data from CAS Common Chemistry Search API
url <- paste0(SEARCH_BASE_URL, smi_bd)
search_response <- GET(url, add_headers("X-API-KEY" = Sys.getenv("CAS_API_KEY")))
smi_search_data <- fromJSON(rawToChar(search_response$content))
# Get results count
smi_search_data$count
## [1] 7
# Display CAS RNs
smi_search_data$results$rn
## [1] "106-99-0" "16422-75-6" "26952-74-9" "29406-96-0" "29989-19-3"
## [6] "31567-90-5" "9003-17-2"
names <- lapply(smi_search_data$results$rn, function(casrn) {
smi_url <- paste0(DETAIL_BASE_URL, "cas_rn=", casrn)
smi_data <- GET(smi_url, add_headers("X-API-KEY" = Sys.getenv("CAS_API_KEY")))
smi_data <- fromJSON(rawToChar(smi_data$content))
Sys.sleep(1)
return(smi_data$name)
})
unlist(names) # Display names
## [1] "1,3-Butadiene"
## [2] "Butadiene trimer"
## [3] "Butadiene dimer"
## [4] "1,3-Butadiene, homopolymer, isotactic"
## [5] "1,3-Butadiene-<em>1</em>,<em>1</em>,<em>2</em>,<em>3</em>,<em>4</em>,<em>4</em>-<em>d</em><sub>6</sub>, homopolymer"
## [6] "Syndiotactic polybutadiene"
## [7] "Polybutadiene"
Handle Multiple Page Results#
The CAS Common Chemistry API returns 50 results per page, and only the first page is returned by default. If the search returns more than 50 results, the offset option can be added to page through and obtain all results:
# Set up search query parameters
n <- "selen*"
# Calculate number of pages needed
num_results <- 200 # Total number of results to retrieve
page_size <- 50 # Number of results to retrieve per page
num_pages <- ceiling(num_results / page_size)
# Create empty list for results
n_search_data <- list()
# Loop through the pages of data
for (page_idx in seq_len(num_pages)) {
# Calculate the starting offset for the current page
offset <- (page_idx - 1) * page_size
# Construct the search URL for the current page
search_url <- paste0(SEARCH_BASE_URL, n, "&offset=", offset)
# Make the API request and append the response to the list of results
response <- GET(search_url, add_headers("X-API-KEY" = Sys.getenv("CAS_API_KEY")))
n_search_data[[page_idx]] <- fromJSON(rawToChar(response$content))
# Pause for 1 second
Sys.sleep(1)
}
# Length of search data includes a top level list for each query
length(n_search_data)
## [1] 4
# List within lists contain the results
length(n_search_data[[1]]$results)
## [1] 3
length(n_search_data[[2]]$results)
## [1] 3
length(n_search_data[[3]]$results)
## [1] 3
length(n_search_data[[4]]$results)
## [1] 3
# We can index and extract out the first CAS RN as follows
n_search_data[[1]]$results[[1]][[1]]
## [1] "10025-68-0"
n_casrn_list <- character()
for (n_idx in seq_along(n_search_data)) {
n_casrn_list <- c(n_casrn_list, n_search_data[[n_idx]]$results$rn)
}
length(n_casrn_list)
## [1] 195
n_casrn_list[1:10]
## [1] "10025-68-0" "10026-03-6" "10026-23-0" "10101-96-9" "10102-18-8"
## [6] "10102-23-5" "10112-94-4" "10161-84-9" "10214-40-1" "10236-58-5"
We can now iterate through the list of CAS RNs to store data about the chemicals. In this example we extract a list of molecularMass values and visualize them.
# Now we can loop through each CAS RN and use the detail API to obtain the entire record
# This will query CAS Common Chem 191 times and take ~ 5 min.
DETAIL_BASE_URL <- "https://commonchemistry.cas.org/api/detail?"
n_detail_data <- list()
for (casrn in n_casrn_list) {
casrn_url <- paste0(DETAIL_BASE_URL, "cas_rn=", casrn)
response <- GET(casrn_url, add_headers("X-API-KEY" = Sys.getenv("CAS_API_KEY")))
n_detail_data[[casrn]] <- content(response)
Sys.sleep(1) # Add a 1 second delay between API calls
}
mms <- list()
for (mm_idx in seq_along(n_detail_data)) {
mms <- c(mms, n_detail_data[[mm_idx]]$molecularMass)
}
head(mms, n = 10)
## [[1]]
## [1] "228.82"
##
## [[2]]
## [1] "220.77"
##
## [[3]]
## [1] "187.91"
##
## [[4]]
## [1] "187.67"
##
## [[5]]
## [1] "174.95"
##
## [[6]]
## [1] "371.11"
##
## [[7]]
## [1] "167.96"
##
## [[8]]
## [1] "300.24"
##
## [[9]]
## [1] "192.52"
##
## [[10]]
## [1] "168.05"
Visualization#
Using the list of molecular mass values we collected, we can create visualizations in R using the hist() and density() functions.
# Histogram
hist(as.numeric(mms), # Reformat mms type to numeric and call as x variable
breaks = 20,
main = "Histogram of available molecularMass values", # Set title
xlab = "Molecular Mass", # X Label
ylab = "Count", # Y Label
col = "plum", ) # Color
We can also make a kernel density plot, a graph that uses a continuous curve to depict the distribution of the data with respect to the molecular mass values. This will give some more fidelity as opposed to the histogram.
d <- density(na.omit(as.numeric(mms)))
plot(d, main = "Kernel Density of Available mms values")
polygon(d, col = "plum", border = "black")
