PubChemR: An R Package for Accessing Chemical Data from PubChem
Selcuk Korkmaz
Trakya Universityselcukkorkmaz@trakya.edu.tr
Bilge Eren Yamasan
Trakya Universityberenyamasan@trakya.edu.tr
Dincer Goksuluk
Erciyes Universitydincergoksuluk@erciyes.edu.tr
2025-05-20
Abstract
Chemical data is a cornerstone in the fields of chemistry, pharmacology, bioinformatics, and environmental science. The PubChemR package provides a comprehensive R interface to the PubChem database, which is one of the largest and most complete repositories of chemical data. This package simplifies the process of querying and retrieving chemical information, including compound structures, properties, biological activities, and more, directly from within R. By leveraging PubChemR, users can programmatically access a wealth of chemical data, which is essential for research and analysis in the chemical sciences. The package supports various functionalities such as searching by chemical identifiers, downloading chemical structures, and retrieving bioassay results, among others. PubChemR is designed to be user-friendly, providing a intuitive experience for R users ranging from academic researchers to practitioners across various scientific disciplines. This paper presents the capabilities of PubChemR, demonstrates its use through practical examples, and discusses its potential impact on chemical data analysis.
Introduction
Chemical data serves as a foundational element in a wide spectrum of scientific research fields, from pharmacology and medicinal chemistry to materials science and environmental studies. The ability to access, query, and manipulate chemical information efficiently is essential for researchers and practitioners who rely on data-driven methodologies to advance their work. PubChem, hosted by the National Center for Biotechnology Information (NCBI), stands as one of the largest publicly available repositories of chemical data, offering free access to an abundance of information on chemical substances, compounds, and biological activities (Wang et al. 2009, 2012; Chen, Wild, and Guha 2009; Li et al. 2010; Kim et al. 2016).
The package for R provides a comprehensive interface to the PubChem database, allowing users to programmatically retrieve and utilize chemical data within the R environment (Korkmaz, Yamasan, and Goksuluk 2024). This integration facilitates a more streamlined workflow for scientists who use R for statistical analysis, data visualization, and computational modeling. By utilizing the package, users can perform a variety of tasks such as searching for chemical substances, fetching compound properties, and obtaining assay data for bioactivity analysis.
Among the extensive range of R packages used in scientific research, (Szöcs et al. 2020) and (Cao et al. 2008) have been important for accessing chemical databases. provides tools for cheminformatics in R, enabling detailed handling and analysis of chemical data. supports access to multiple chemical databases. Additionally, other notable packages like , , , , and offer robust functionalities for chemical data manipulation and analysis. The package interfaces with the Chemistry Development Kit (CDK), providing molecular structure parsing and descriptor calculation (Guha 2007). interfaces with OpenBabel for chemical format conversions and molecular property calculations (Horan and Girke 2024). facilitates identifier mapping across biological databases, enhancing data integration (Leemans et al. 2024). supports the creation and handling of mass spectrometry databases, crucial for compound identification (Stravs et al. 2013). ensures accurate lipid nomenclature in lipidomics studies (Kopczynski et al. 2020).
However, when it comes to direct interaction with the PubChem database, these packages have limitations, often requiring users to work through complex API documentation or use additional tools. To address these issues, , designed specifically for the PubChem database, complements the functionalities of these packages. It simplifies accessing chemical data from PubChem, using functions that make API interactions more straightforward. Users can retrieve data easily by calling these functions with the right parameters. Unlike , which works with various databases, and , which covers a wide range of cheminformatics tasks, is focused solely on PubChem, allowing for more efficient and targeted data interactions.
Here, we introduce , detailing its functionality, design principles, and potential use cases. We will demonstrate how can be utilized to enhance research workflows and provide examples of its application in real-world scenarios. By the end of this paper, readers will be equipped with the knowledge to integrate into their data analysis toolkit, unlocking the potential to drive forward chemical and biological research with the power of R.
Design
The design of is driven by the need for a seamless and intuitive interface for R users to access the vast chemical data available in the PubChem database. Our design principles focus on simplicity, efficiency, and robustness:
Simplicity: The package is designed to minimize the complexity of interacting with the PubChem API. Functions are named and structured to be self-explanatory, allowing users to intuitively understand their purposes.
Efficiency: Considering the extensive size of the PubChem database, we optimized for speed and minimal resource consumption. Our design includes efficient handling of API calls and data processing.
Robustness: The package is designed to handle a wide range of user queries, from simple compound searches to complex data extractions. Exception handling and error reporting are integral, ensuring users are informed of issues in their queries or data processing.
Implementation
The implementation of involved several key steps:
API Integration: We integrated the PubChem API using R’s HTTP client capabilities. This involved mapping the PubChem’s RESTful services into R functions.
Data Processing: The raw data from PubChem API calls are processed and transformed into user-friendly R data structures such as data frames and lists.
Function Development: Each function in corresponds to a specific type of query or data retrieval from the PubChem database, with parameters allowing for flexible and targeted searches.
Testing and Validation: We performed rigorous testing to ensure accuracy and efficiency using the testthat package for unit testing each function. This approach helps us define and verify expected behavior, catch errors, and integrate well with our workflow, thereby maintaining high code quality and reliability.
Use cases
The package interfaces with the PubChem database through URL syntax, with each query within this syntax serving as an argument in the designated function (Figure 1).
Interfacing the PubChem’s PUG-REST database with the PubChemR package through URL syntax: utilizing queries as function arguments
The functions in are designed with flexibility in mind, allowing users to specify the type of information they need and the format in which they wish to receive it. For instance, data can be returned as R objects like data frames or lists, ready for analysis.
pacakage can be installed from CRAN (The Comprehensive R Archive Network) and loaded as follow:
# install.packages("PubChemR", repos = "http://cran.us.r-project.org")
library("PubChemR")The package is currently in a state of active development. The latest version in development can be accessed via GitHub (https://github.com/selcukorkmaz/PubChemR). This paper was composed utilizing version 2.1.
Most functions in the package require three main arguments; domain, namespace, and identifier.
1. Domain: This represents the primary classification within the PubChem system that dictates the type of data being accessed. Examples of domains include “substance,” “compound,” “assay,” “gene,” and others. Each domain encapsulates specific types of scientific data, such as chemical compounds or genetic information.
2. Namespace: Within each domain, the namespace further specifies the method or criteria for querying the data. It acts as a sub-category within the domain that allows for more refined searches. For instance, in the compound domain, namespaces can be specific identifiers like “cid” for compound ID, or “name” for the compound’s common name, among others.
3. Identifier: These are the actual data values used to perform the query. Identifiers can be vector of positive integers (e.g. cid, sid, aid) or strings (e.g. name, smiles, source, inchikey, formula). They are the key pieces of information that pinpoint the exact record or set of records to be retrieved from the database.
Additionally, the optional arguments “operation” and “searchtype” play crucial roles in refining the scope and focus of data queries. The “operation” argument specifies the type of data processing or retrieval task that should be performed on the identified records. For instance, operations can range from fetching complete data records to retrieving specific properties or summaries of compounds, genes, or assays. This flexibility allows users to access both broad overviews and detailed attributes of database entries according to their research needs. Meanwhile, the “searchtype” argument defines the method of search being employed. It could be a structured search, such as substructure or similarity search, which is essential for identifying compounds with particular chemical structures or features. These optional arguments enhance the API’s versatility, enabling researchers to tailor queries more precisely and retrieve data that best fits their experimental and analytical requirements. For more detailed information, please refer to the official documentation at https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest.
Table 1 provides detailed information about four key arguments: “domain,” “searchtype,” “namespace,” and “operation.” This table is designed to help users understand how to effectively utilize these components to customize queries within the PubChemR package.
| domain | searchtype | namespace | operation |
|---|---|---|---|
| substance | - | sid, sourceid/<source id>, sourceall/<source name>, name, <xref>, listkey | record, synonyms, sids, cids, aids, assaysummary, classification, <xrefs>, description |
| compound | <structure search> = {substructure, superstructure, similarity, identity}/{smiles, inchi, sdf, cid} <fast search> = {fastidentity, fastsimilarity_2d, fastsimilarity_3d, fastsubstructure, fastsuperstructure}/{smiles, smarts, inchi, sdf, cid}, fastformula | cid, name, smiles, inchi, sdf, inchikey, formula, <structure search>, <xref>, listkey, <fast search> | record, <compound property>, synonyms, sids, cids, aids, assaysummary, classification, <xrefs>, description, conformers |
| assay | - | aid, listkey, type/<assay type>, sourceall/<source name>, target/<assay target>, activity/<activity column name> | record, concise, aids, sids, cids, description, targets/<target type>, <doseresponse>, summary, classification |
| gene | - | geneid, genesymbol, synonym | summary, aids, concise, pwaccs |
| protein | - | accession, gi, synonym | summary, aids, concise, pwaccs |
| pathway | - | pwacc | summary, cids, geneids, accessions |
| taxonomy | - | taxid, synonym | summary, aids |
| cell | - | cellacc, synonym | summary, aids |
Table 1: Overview of key arguments in the PubChemR package
In the following sections, we will explore each function in detail, examining the parameters they accept, the type of data they return, and providing examples to illustrate their use. These examples will serve as a guide for users to understand how to effectively utilize the package to access and manipulate chemical and biological data for their specific needs.
First, we will concentrate on three primary functions, each focusing
on a specific domain: get_compounds to retrieve compound
data, get_substances to extract substance data, and
get_assays to fetch assay data. These functions are capable
of handling multiple queries simultaneously and return a
PubChemInstanceList class. This is a specialized class
specifically created for these functions to manage the complex PubChem
data efficiently. After utilizing each of these functions, we will
employ the instance function. This function is designed to
retrieve detailed information about a compound from a
PubChemInstanceList. It provides comprehensive details about
the specific compound, including its instance details (i.e., slots).
Finally, we will implement the retrieve function with the
relevant slots to extract specific data elements from the compound data.
This approach ensures that we can precisely access the required
information from the vast amount of data available, thereby enhancing
the efficiency and effectiveness of our data analysis process.
Next, we will fetch a variety of compound properties, such as
molecular weight, chemical formula, isomeric SMILES, and more, using the
get_properties function. Additionally, we will focus on two
functions for downloading data from the PubChem database:
get_sdf and download. The get_sdf
function is specifically designed to download chemical structure data in
the widely recognized Structure Data File (SDF) format. The
download function streamlines the process of accessing and
downloading content from the PubChem database.
Finally, we will introduce two new functions:
get_pug_rest and get_pug_view. The
get_pug_rest function provides a direct and efficient
method for accessing a wide range of chemical data. In contrast, the
get_pug_view function is designed to offer access to
detailed summary reports and additional information that is not usually
included in the primary PubChem Substance, Compound, or BioAssay
records.
Retrieve Compund Information
The get_compounds function allows R users to retrieve
compound information from the PubChem database. This function
specifically targets retrieving compound-related information. This
specialized focus is crucial for users who require direct and efficient
access to detailed compound data, a common need in various fields of
chemical research and analysis. Below, we will demonstrate how to
retrieve compound data using different namespaces:
a. Retrieving Compound Information by Name: The
get_compounds function simplifies the process of retrieving
detailed compound information by using common compound names. This
feature is particularly beneficial in scenarios where the specific CIDs
of compounds are unknown or in educational contexts where common names
are more frequently used. It simplifies the process of data retrieval
for users who may not be familiar with the technical identifiers of
compounds but are well-versed with their common or commercial names.
Consider the following example where the get_compounds
function is employed to fetch data for compounds using their common
names:
compounds <- get_compounds(identifier = c("aspirin", "caffeine", "glucose"), namespace = "name")
compounds##
## An object of class 'PubChemInstanceList'
##
## Number of instances: 3
## - Domain: Compound
## - Namespace: Name
## - Identifier(s): aspirin, caffeine, ... and 1 more.
##
## * Run 'instance(...)' function to extract specific instances from the complete list, and
## 'request_args(...)' to see all the requested instance identifiers.
## * See ?instance and ?request_args for details.The request_args function can be executed to view all
the requested instance identifiers:
request_args(object = compounds)## $namespace
## [1] "name"
##
## $identifier
## [1] "aspirin" "caffeine" "glucose"
##
## $domain
## [1] "compound"
##
## $operation
## NULL
##
## $options
## NULL
##
## $searchtype
## NULLTo retrieve detailed information about a specific compound
(e.g. aspirin), we can use the instance function on the
result:
compound_aspirin <- instance(object = compounds, .which = "aspirin")
compound_aspirin##
## An object of class 'PubChemInstance'
##
## Request Details:
## - Domain: Compound
## - Namespace: Name
## - Identifier: aspirin
##
## Instance Details:
## - id (1): [<named list>] id
## - atoms (2): [<named list>] aid, element
## - bonds (3): [<named list>] aid1, aid2, order
## - coords (1): [<unnamed list>]
## - charge (1): [<unnamed numeric>]
## - props (22): [<unnamed list>]
## - count (10): [<named numeric>] heavy_atom, atom_chiral, atom_chiral_def, atom_chiral_undef, ...
##
## NOTE: Run getter function 'retrieve()' with element name above to extract data from corresponding list.
## See ?retrieve for details.The instance function retrieves detailed information
about the specific compound, including its various components (known as
slots). In this example, the compound has seven slots: id,
atoms, bonds, coords,
charge, props, and count. To
extract specific data elements from the compound data, we can use the
retrieve function with the relevant slots. For example,
using the props slot extracts detailed properties of the
compound, including information such as label, name, data type, release,
value, implementation, version, software, and source. This comprehensive
information covers various physical, chemical, and structural properties
of the compound.
retrieve(object = compound_aspirin, .slot = "props", .to.data.frame = TRUE)## # A tibble: 22 × 11
## Identifier label name datatype release value implementation version software
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 aspirin Comp… Cano… 5 2025.0… 1 <NA> <NA> <NA>
## 2 aspirin Comp… <NA> 7 2025.0… 212 E_COMPLEXITY 3.4.8.… Cactvs
## 3 aspirin Count Hydr… 5 2025.0… 4 E_NHACCEPTORS 3.4.8.… Cactvs
## 4 aspirin Count Hydr… 5 2025.0… 1 E_NHDONORS 3.4.8.… Cactvs
## 5 aspirin Count Rota… 5 2025.0… 3 E_NROTBONDS 3.4.8.… Cactvs
## 6 aspirin Fing… SubS… 16 2025.0… 0000… E_SCREEN 3.4.8.… Cactvs
## 7 aspirin IUPA… Allo… 1 2025.0… 2-ac… <NA> 2.7.0 Lexiche…
## 8 aspirin IUPA… CAS-… 1 2025.0… 2-ac… <NA> 2.7.0 Lexiche…
## 9 aspirin IUPA… Mark… 1 2025.0… 2-ac… <NA> 2.7.0 Lexiche…
## 10 aspirin IUPA… Pref… 1 2025.0… 2-ac… <NA> 2.7.0 Lexiche…
## # ℹ 12 more rows
## # ℹ 2 more variables: source <chr>, parameters <chr>b. Retrieving Compound Information by CID: The
get_compounds function can be used to extract detailed
compound data utilizing CIDs. This feature is particularly advantageous
for researchers who require precise and comprehensive data on specific
compounds. By inputting a vector of CIDs, users can quickly access a
vast amount of information for their research needs.
Here’s an illustrative example demonstrating the use of
get_compounds to obtain data for a set of compounds using
their CIDs:
compounds <- get_compounds(identifier = c(2244, 305), namespace = "cid")
compounds##
## An object of class 'PubChemInstanceList'
##
## Number of instances: 2
## - Domain: Compound
## - Namespace: CID
## - Identifier(s): 2244, 305
##
## * Run 'instance(...)' function to extract specific instances from the complete list, and
## 'request_args(...)' to see all the requested instance identifiers.
## * See ?instance and ?request_args for details.Similarly, we can use the instance function on the
result to retrieve detailed information about CID 2244:
compound_2244 <- instance(object = compounds, .which = 2244)
compound_2244##
## An object of class 'PubChemInstance'
##
## Request Details:
## - Domain: Compound
## - Namespace: CID
## - Identifier: 2244
##
## Instance Details:
## - id (1): [<named list>] id
## - atoms (2): [<named list>] aid, element
## - bonds (3): [<named list>] aid1, aid2, order
## - coords (1): [<unnamed list>]
## - charge (1): [<unnamed numeric>]
## - props (22): [<unnamed list>]
## - count (10): [<named numeric>] heavy_atom, atom_chiral, atom_chiral_def, atom_chiral_undef, ...
##
## NOTE: Run getter function 'retrieve()' with element name above to extract data from corresponding list.
## See ?retrieve for details.Similar to the previous section, we can use the retrieve
function with the element names (i.e. slots) mentioned above to extract
specific data sections.
c. Advanced Search with SMILES: In cheminformatics,
the Simplified Molecular Input Line Entry System (SMILES) is a
widely-used method for representing chemical structures. The
get_compounds function package adeptly handles queries
based on SMILES strings, enabling users to search for compounds by their
structural characteristics. The main advantage of using SMILES is their
precise representation of molecular structures, unlike CIDs or chemical
names that can be ambiguous or inapplicable.
Consider the following example where the get_compounds
function is used to search for compounds using their SMILES strings:
compounds_by_smiles <- get_compounds(identifier = c("CC(=O)OC1=CC=CC=C1C(=O)O",
"CN1C=NC2=C1C(=O)N(C(=O)N2C)C"),
namespace = "smiles")
compounds_by_smiles##
## An object of class 'PubChemInstanceList'
##
## Number of instances: 2
## - Domain: Compound
## - Namespace: SMILES
## - Identifier(s): CC(=O)OC1=CC=CC=C1C(=O)O, CN1C=NC2=C1C(=O)N(C(=O)N2C)C
##
## * Run 'instance(...)' function to extract specific instances from the complete list, and
## 'request_args(...)' to see all the requested instance identifiers.
## * See ?instance and ?request_args for details.In this example, compounds_by_smiles object is a PubChemInstanceList containing data for compounds that correspond to the provided SMILES strings. The strings “CC(=O)OC1=CC=CC=C1C(=O)O” and “CN1C=NC2=C1C(=O)N(C(=O)N2C)C” represent the molecular structures of aspirin and caffeine, respectively. The function returns a PubChemInstanceList where each element contains comprehensive information about these compounds.
Let’s only access data for CC(=O)OC1=CC=CC=C1C(=O)O using the instance function.
compound_smiles <- instance(object = compounds_by_smiles, .which = "CC(=O)OC1=CC=CC=C1C(=O)O")
compound_smiles##
## An object of class 'PubChemInstance'
##
## Request Details:
## - Domain: Compound
## - Namespace: SMILES
## - Identifier: CC(=O)OC1=CC=CC=C1C(=O)O
##
## Instance Details:
## - id (1): [<named list>] id
## - atoms (2): [<named list>] aid, element
## - bonds (3): [<named list>] aid1, aid2, order
## - coords (1): [<unnamed list>]
## - charge (1): [<unnamed numeric>]
## - props (22): [<unnamed list>]
## - count (10): [<named numeric>] heavy_atom, atom_chiral, atom_chiral_def, atom_chiral_undef, ...
##
## NOTE: Run getter function 'retrieve()' with element name above to extract data from corresponding list.
## See ?retrieve for details.Finally, we can utilize the retrieve function to access
data in each slot of the compound_smiles object, as
demonstrated earlier.
Retrieve Substance Information
The get_substances function is for researchers looking
to explore the extensive substance records in the PubChem database.
PubChem’s definition of substances encompasses a broad spectrum of
chemical entities, ranging from unique samples of individual chemical
compounds to complex mixtures. These substances often manifest in
diverse forms, including but not limited to different salts, isotopes,
complexes, or combinations of various compounds.
The get_substances function is designed to meet the
specific requirements of accessing a wide range of substance records. It
uses unique substance identifiers (SIDs) for each substance in PubChem,
allowing users to easily and accurately retrieve detailed information.
This feature is especially important in situations where understanding
the differences between various forms or versions of a compound is
critical, such as in drug research, managing chemical databases, or
meeting regulatory standards.
In the following example, we will retrieve substance data for three substances using their SIDs:
Aspirin (SID: 103164874): Aspirin, also known as acetylsalicylic acid, is widely used as an analgesic to relieve pain, reduce fever, and act as an anti-inflammatory medication. As a substance, it encompasses various forms and preparations of aspirin available from different sources.
Caffeine (SID: 403385742): Caffeine is a central nervous system stimulant commonly found in coffee, tea, and various energy drinks. As a substance, it includes different sources and formulations of caffeine beyond its pure chemical structure.
Glucose (SID: 403435554): Glucose is a simple sugar that serves as a primary energy source for living organisms. As a substance, it includes various forms and sources of glucose, providing detailed information beyond the pure compound.
Using the get_substances function, we can access
detailed records for these substances in the PubChem database by
providing their SIDs.
substances <- get_substances(identifier = c(103164874, 403385742, 403435554),
namespace = "sid")
substances##
## An object of class 'PubChemInstanceList'
##
## Number of instances: 3
## - Domain: Substance
## - Namespace: SID
## - Identifier(s): 103164874, 403385742, ... and 1 more.
##
## * Run 'instance(...)' function to extract specific instances from the complete list, and
## 'request_args(...)' to see all the requested instance identifiers.
## * See ?instance and ?request_args for details.Next, let’s fetch detailed substance information for glucose (SID: 403435554). First, we need to run the instance function to see the slots that contain substance data:
instance(object = substances, .which = 403435554)##
## Substance Data from PubChem Database
##
## Request Details:
## - Domain: Substance
## - Namespace: SID
## - Identifier: 403435554
##
## Number of substances retrieved: 1
##
## Substances contain data within following slots;
## - sid (2): [<named numeric>] id, version
## - source (1): [<named list>] db
## - xref (3): [<unnamed list>]
## - compound (2): [<unnamed list>]
##
## NOTE: Run getter function 'retrieve()' with element name above to extract data from corresponding list.
## See ?retrieve for details.The output shows four slots that contain substance data for glucose.
We can retrieve data from each slot using the retrieve
function. For example, we can extract detailed compound information,
such as the compound ID, atom IDs and elements, charges, bond details,
and coordinates for molecular structure visualization:
retrieve(object = substances, .which = 403435554, .slot = "compound", .to.data.frame = FALSE)## $Identifier
## [1] 403435554
##
## [[2]]
## [[2]]$id
## type
## 0
##
## [[2]]$atoms
## [[2]]$atoms$aid
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
##
## [[2]]$atoms$element
## [1] 8 8 8 8 8 6 6 8 6 6 6 6 1 1 1 1
##
##
## [[2]]$bonds
## [[2]]$bonds$aid1
## [1] 6 7 7 8 9 9 10 10 10 11 11 11 12 12 12 12
##
## [[2]]$bonds$aid2
## [1] 5 6 13 7 4 8 3 9 16 2 10 15 1 7 11 14
##
## [[2]]$bonds$order
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##
##
## [[2]]$coords
## [[2]]$coords[[1]]
## [[2]]$coords[[1]]$type
## [1] 1 3
##
## [[2]]$coords[[1]]$aid
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
##
## [[2]]$coords[[1]]$conformers
## [[2]]$coords[[1]]$conformers[[1]]
## [[2]]$coords[[1]]$conformers[[1]]$x
## [1] -1.1936 0.3873 2.0000 2.0000 -2.0000 -1.1936 -0.3873 0.3873 1.1936
## [10] 1.1936 0.3873 -0.3873 -0.9849 -0.9129 -0.1439 1.7192
##
## [[2]]$coords[[1]]$conformers[[1]]$y
## [1] -0.4492 -1.3794 -0.4492 1.3794 0.9143 1.3794 0.9143 1.3794 0.9143
## [10] 0.0144 -0.4492 0.0144 0.8106 0.3182 -0.7429 0.3168
##
## [[2]]$coords[[1]]$conformers[[1]]$style
## [[2]]$coords[[1]]$conformers[[1]]$style$annotation
## [1] 6 5 6 5
##
## [[2]]$coords[[1]]$conformers[[1]]$style$aid1
## [1] 7 10 11 12
##
## [[2]]$coords[[1]]$conformers[[1]]$style$aid2
## [1] 13 16 15 14
##
##
##
##
##
##
## [[2]]$charge
## [1] 0
##
##
## [[3]]
## [[3]]$id
## [[3]]$id$type
## [1] 1
##
## [[3]]$id$id
## cid
## 5793Besides SIDs, the function supports additional namespaces such as sourceid, sourceall, name, xref, and listkey. This flexibility enables researchers to access substance records from a range of perspectives, depending on their available data or specific requirements.
Retrieve Assay Information
The get_assays function is another key function for
researchers needing detailed biological assay information from the
PubChem database. These assays, essential for drug discovery,
toxicology, and pharmacology, measure biological activities of
substances. The function simplifies accessing this data, crucial for
bioinformatics and cheminformatics (Korkmaz 2020;
Yamasan and Korkmaz 2024). It enables customized queries in the
PubChem database, allowing users to explore a wide range of assay data,
including drug efficacy and toxicity. Its versatility in handling
different parameters facilitates a specific data retrieval approach.
The utility of the get_assays function extends across
multiple research scenarios. In drug discovery, It enables researchers
to explore assays related to potential drug compounds, aiding in the
comprehension of their efficacy and safety profiles. In toxicological
studies, the function is key in acquiring insights into the toxic
effects of diverse substances.
In the field of scientific research, particularly in areas such as pharmacology, toxicology, and biochemistry, researchers frequently encounter specific assays that are of interest to their studies. These assays are often identified by their Assay IDs (AIDs), which are referenced in scientific literature or various databases. The get_assays function provides a direct and efficient means for researchers to access comprehensive information about these particular assays.
Utilizing the get_assays function, researchers can input
a vector of AIDs to retrieve detailed data about each corresponding
assay. This functionality is especially beneficial for those who need to
analyze and interpret assay data as part of their research projects or
for educational purposes.
Consider the following practical example where we retrieve assay data from PubChem using their AIDs.
First, we use the get_assays function to retrieve data
for the specified assays. The identifier parameter is a vector of AIDs
(485314, 485341, 504466, 624202, and 651820), and the namespace
parameter specifies that the identifiers are AIDs.
assays <- get_assays(identifier = c(485314, 485341, 504466, 624202, 651820),
namespace = "aid")
assays##
## An object of class 'PubChemInstanceList'
##
## Number of instances: 5
## - Domain: Assay
## - Namespace: AID
## - Identifier(s): 485314, 485341, ... and 3 more.
##
## * Run 'instance(...)' function to extract specific instances from the complete list, and
## 'request_args(...)' to see all the requested instance identifiers.
## * See ?instance and ?request_args for details.The output shows that the assays object is of class ‘PubChemInstanceList’ and contains data for 5 assays identified by their AIDs. It includes information about the number of instances (5 in this case), the domain (Assay), the namespace (AID), and the specific identifiers. The output also provides hints on how to proceed further.
Now, we can use the instance function to extract
specific instances from the complete list. For example, the following
code extracts the detailed data for the assay with identifier 485314
from the list of assays:
assay_485314 <- instance(object = assays, .which = 485314)
assay_485314##
## An object of class 'PubChemInstance'
##
## Request Details:
## - Domain: Assay
## - Namespace: AID
## - Identifier: 485314
##
## Instance Details:
## - aid (2): [<named numeric>] id, version
## - aid_source (1): [<named list>] db
## - name (1): [<unnamed character>]
## - description (3): [<unnamed character>]
## - protocol (1): [<unnamed character>]
## - comment (4): [<unnamed character>]
## - xref (4): [<unnamed list>]
## - results (23): [<unnamed list>]
## - revision (1): [<unnamed numeric>]
## - target (1): [<unnamed list>]
## - activity_outcome_method (1): [<unnamed numeric>]
## - dr (1): [<unnamed list>]
## - grant_number (1): [<unnamed character>]
## - project_category (1): [<unnamed numeric>]
##
## NOTE: Run getter function 'retrieve()' with element name above to extract data from corresponding list.
## See ?retrieve for details.The output provides a detailed view of the assay_485314 object, which is of class ‘PubChemInstance’. It includes both request details and instance details
The output also notes that we can use the retrieve
function with the element names above to extract data from the
corresponding list. For example, we can retrieve the results of the
assay, providing detailed data on the outcomes observed during the
assay:
retrieve(object = assays, .which = 485314, .slot = "results")## # A tibble: 41 × 8
## Identifier tid name description type unit ac tc
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 485314 1 Phenotype Indicates type… 4 254 <NA> <NA>
## 2 485314 2 Potency Concentration … 1 5 TRUE <NA>
## 3 485314 3 Efficacy Maximal effica… 1 15 <NA> <NA>
## 4 485314 4 Analysis Comment Annotation/not… 4 254 <NA> <NA>
## 5 485314 5 Curve_Description A description … 4 254 <NA> <NA>
## 6 485314 6 Fit_LogAC50 The logarithm … 1 254 <NA> <NA>
## 7 485314 7 Fit_HillSlope The Hill slope… 1 254 <NA> <NA>
## 8 485314 8 Fit_R2 R^2 fit value … 1 254 <NA> <NA>
## 9 485314 9 Fit_InfiniteActivity The asymptotic… 1 15 <NA> <NA>
## 10 485314 10 Fit_ZeroActivity Efficacy at ze… 1 15 <NA> <NA>
## # ℹ 31 more rowsBy following these steps and using the retrieve
function, we can access detailed and specific data from the assay
records in PubChem. This comprehensive approach allows researchers to
gather all necessary information about assays, aiding in their analysis
and research activities.
Retrieve Property Information
The get_properties is another important function for
researchers who require access to specific chemical property data from
the PubChem database. This function is designed with the aim of
simplifying the process of querying PubChem for a variety of compound
properties, such as molecular weight, chemical formula, isomeric SMILES,
and more. It is particularly useful for those in need of detailed
chemical information across a range of compounds.
The get_properties function allows users to specify a
set of properties and the identifiers of the compounds for which these
properties are required. The function then queries the PubChem database
and retrieves the requested data.
Consider the following practical application of the
get_properties function:
First, we use the get_properties function to retrieve
specific properties for several compounds identified by their names:
props <- get_properties(
properties = c("MolecularWeight", "MolecularFormula", "InChI"),
identifier = c("aspirin", "caffeine", "glucose"),
namespace = "name"
)In this example, the properties parameter specifies that we want to retrieve the molecular weight, molecular formula, and InChI for each compound. The identifier parameter lists the common names of the compounds (aspirin, caffeine, and glucose), and the namespace parameter indicates that these identifiers are compound names. The function fetches the specified properties from the PubChem database.
To extract the properties for a specific compound, such as aspirin,
we can use the retrieve function:
retrieve(instance(props, "aspirin"), .slot = NULL)## # A tibble: 1 × 6
## Identifier CID MolecularFormula MolecularWeight InChI InChIKey
## <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 aspirin 2244 C9H8O4 180.16 InChI=1S/C9H8O4/c1… BSYNRYM…This tibble shows the identifier (aspirin), CID (2244), molecular formula (C9H8O4), molecular weight (180.16), InChI, and InChIKey.
To combine the properties of all compounds into a single data frame,
we set .combine.all as TRUE:
retrieve(props, .combine.all = TRUE)## # A tibble: 3 × 6
## Identifier CID MolecularFormula MolecularWeight InChI InChIKey
## <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 aspirin 2244 C9H8O4 180.16 InChI=1S/C9H8O4/c1… BSYNRYM…
## 2 caffeine 2519 C8H10N4O2 194.19 InChI=1S/C8H10N4O2… RYYVLZV…
## 3 glucose 5793 C6H12O6 180.16 InChI=1S/C6H12O6/c… WQZGKKK…This tibble shows the identifier, CID, molecular formula, molecular weight, InChI, and InChIKey for aspirin, caffeine, and glucose.
To return only the selected properties for all compounds, we can
specify the properties using the .slot argument. The output
will be a tibble with only the molecular weight and molecular formula
for each compound:
retrieve(props, .combine.all = TRUE,
.slot = c("MolecularWeight", "MolecularFormula"))## Identifier MolecularWeight MolecularFormula
## 1 aspirin 180.16 C9H8O4
## 2 caffeine 194.19 C8H10N4O2
## 3 glucose 180.16 C6H12O6There are 40 compound properties that we can fetch from the PubChem:
property_map(type = "all")## [1] "MolecularFormula" "MolecularWeight"
## [3] "CanonicalSMILES" "IsomericSMILES"
## [5] "InChI" "InChIKey"
## [7] "IUPACName" "XLogP"
## [9] "ExactMass" "MonoisotopicMass"
## [11] "TPSA" "Complexity"
## [13] "Charge" "HBondDonorCount"
## [15] "HBondAcceptorCount" "RotatableBondCount"
## [17] "HeavyAtomCount" "IsotopeAtomCount"
## [19] "AtomStereoCount" "DefinedAtomStereoCount"
## [21] "UndefinedAtomStereoCount" "BondStereoCount"
## [23] "DefinedBondStereoCount" "UndefinedBondStereoCount"
## [25] "CovalentUnitCount" "Volume3D"
## [27] "ConformerModelRMSD3D" "ConformerModelRMSD3D"
## [29] "XStericQuadrupole3D" "YStericQuadrupole3D"
## [31] "ZStericQuadrupole3D" "FeatureCount3D"
## [33] "FeatureAcceptorCount3D" "FeatureDonorCount3D"
## [35] "FeatureAnionCount3D" "FeatureCationCount3D"
## [37] "FeatureRingCount3D" "FeatureHydrophobeCount3D"
## [39] "EffectiveRotorCount3D" "ConformerCount3D"The type argument in the property_map
function determines the method of searching within the available
properties. Setting type = "contain" will retrieve all
properties that include the strings specified in the properties
argument. In the following example, we fetch properties for a range of
CIDs from 2244 to 2260. The properties argument includes the keywords
“mass” and “molecular”, and the propertyMatch argument is
set to type = "contain". This setup ensures that the
function retrieves any properties containing the specified keywords.
props <- get_properties(
properties = c("mass", "molecular"),
identifier = 2244:2260,
namespace = "cid",
propertyMatch = list(
type = "contain"
)
)
retrieve(props, .combine.all = TRUE, .to.data.frame = TRUE)## # A tibble: 16 × 6
## Identifier CID MolecularFormula MolecularWeight ExactMass MonoisotopicMass
## <int> <dbl> <chr> <chr> <chr> <chr>
## 1 2244 2244 C9H8O4 180.16 180.04225… 180.04225873
## 2 2245 2245 C21H27N5O7S 493.5 493.16311… 493.16311939
## 3 2246 2246 C40H52O4 596.8 596.38656… 596.38656014
## 4 2247 2247 C28H31FN4O 458.6 458.24818… 458.24818979
## 5 2248 2248 C17H35N5O6 405.5 405.25873… 405.25873385
## 6 2249 2249 C14H22N2O3 266.34 266.16304… 266.16304257
## 7 2250 2250 C33H35FN2O5 558.6 558.25300… 558.25300038
## 8 2252 2252 C10H16N2O4 228.24 228.11100… 228.11100700
## 9 2253 2253 C10H16N2O4 228.24 228.11100… 228.11100700
## 10 2254 2254 C30H44O16S2-2 724.8 724.20707… 724.20707766
## 11 2255 2255 C30H46O16S2 726.8 726.22272… 726.22272772
## 12 2256 2256 C8H14ClN5 215.68 215.09377… 215.0937732
## 13 2257 2257 C22H20N3O9+3 470.4 470.11995… 470.11995422
## 14 2258 2258 C22H17N3O9 467.4 467.09647… 467.09647913
## 15 2259 2259 C22H14O9 422.3 422.06378… 422.06378202
## 16 2260 2260 C44H62N2O12 811.0 810.43027… 810.43027542Moreover, we can extract properties that start or end with specific
strings. In the following example, we fetch properties that start with
the word “molecular.” Here, the type parameter is set to
"start", and the .ignore.case parameter is set
to TRUE to make the search case-insensitive.
props <- get_properties(
properties = "molecular",
identifier = 2244:2260,
namespace = "cid",
propertyMatch = list(
type = "start",
.ignore.case = TRUE
)
)
retrieve(props, .combine.all = TRUE, .to.data.frame = TRUE)## # A tibble: 17 × 4
## Identifier CID MolecularFormula MolecularWeight
## <int> <dbl> <chr> <chr>
## 1 2244 2244 C9H8O4 180.16
## 2 2245 2245 C21H27N5O7S 493.5
## 3 2246 2246 C40H52O4 596.8
## 4 2247 2247 C28H31FN4O 458.6
## 5 2248 2248 C17H35N5O6 405.5
## 6 2249 2249 C14H22N2O3 266.34
## 7 2250 2250 C33H35FN2O5 558.6
## 8 2251 2251 C10H13N5O13P3-3 504.16
## 9 2252 2252 C10H16N2O4 228.24
## 10 2253 2253 C10H16N2O4 228.24
## 11 2254 2254 C30H44O16S2-2 724.8
## 12 2255 2255 C30H46O16S2 726.8
## 13 2256 2256 C8H14ClN5 215.68
## 14 2257 2257 C22H20N3O9+3 470.4
## 15 2258 2258 C22H17N3O9 467.4
## 16 2259 2259 C22H14O9 422.3
## 17 2260 2260 C44H62N2O12 811.0This output indicates that “MolecularFormula” and “MolecularWeight” are the properties available in PubChem that start with the word “molecular.”
Next, we extract properties that end with the word “mass.” Here, the
type parameter is set to "end", and the
.ignore.case parameter is set to TRUE to make
the search case-insensitive.
props <- get_properties(
properties = "mass",
identifier = 2244:2260,
namespace = "cid",
propertyMatch = list(
type = "end",
.ignore.case = TRUE
)
)
retrieve(props, .combine.all = TRUE, .to.data.frame = TRUE)## # A tibble: 17 × 4
## Identifier CID ExactMass MonoisotopicMass
## <int> <dbl> <chr> <chr>
## 1 2244 2244 180.04225873 180.04225873
## 2 2245 2245 493.16311939 493.16311939
## 3 2246 2246 596.38656014 596.38656014
## 4 2247 2247 458.24818979 458.24818979
## 5 2248 2248 405.25873385 405.25873385
## 6 2249 2249 266.16304257 266.16304257
## 7 2250 2250 558.25300038 558.25300038
## 8 2251 2251 503.97227148 503.97227148
## 9 2252 2252 228.11100700 228.11100700
## 10 2253 2253 228.11100700 228.11100700
## 11 2254 2254 724.20707766 724.20707766
## 12 2255 2255 726.22272772 726.22272772
## 13 2256 2256 215.0937732 215.0937732
## 14 2257 2257 470.11995422 470.11995422
## 15 2258 2258 467.09647913 467.09647913
## 16 2259 2259 422.06378202 422.06378202
## 17 2260 2260 810.43027542 810.43027542This output indicates that “ExactMass” and “MonoisotopicMass” are the properties available in PubChem that end with the word “mass.”
Finally, to return all available properties of the requested compounds, set properties = NULL and the propertyMatch argument to type = “all”.
props <- get_properties(
properties = NULL,
identifier = 2244:2260,
namespace = "cid",
propertyMatch = list(
type = "all"
)
)
retrieve(props, .combine.all = TRUE)## # A tibble: 17 × 41
## Identifier CID MolecularFormula MolecularWeight SMILES ConnectivitySMILES
## <int> <dbl> <chr> <chr> <chr> <chr>
## 1 2244 2244 C9H8O4 180.16 CC(=O)O… CC(=O)OC1=CC=CC=C…
## 2 2245 2245 C21H27N5O7S 493.5 CC1(C(N… CC1(C(N2C(S1)C(C2…
## 3 2246 2246 C40H52O4 596.8 CC1=C(C… CC1=C(C(CC(C1=O)O…
## 4 2247 2247 C28H31FN4O 458.6 COC1=CC… COC1=CC=C(C=C1)CC…
## 5 2248 2248 C17H35N5O6 405.5 CC(C1CC… CC(C1CCC(C(O1)OC2…
## 6 2249 2249 C14H22N2O3 266.34 CC(C)NC… CC(C)NCC(COC1=CC=…
## 7 2250 2250 C33H35FN2O5 558.6 CC(C)C1… CC(C)C1=C(C(=C(N1…
## 8 2251 2251 C10H13N5O13P3-3 504.16 C1=NC(=… C1=NC(=C2C(=N1)N(…
## 9 2252 2252 C10H16N2O4 228.24 CC(C)(C… CC(C)(C)C1=C(C(=O…
## 10 2253 2253 C10H16N2O4 228.24 CC(C)(C… CC(C)(C)C1=C(C(=O…
## 11 2254 2254 C30H44O16S2-2 724.8 CC(C)CC… CC(C)CC(=O)OC1C(C…
## 12 2255 2255 C30H46O16S2 726.8 CC(C)CC… CC(C)CC(=O)OC1C(C…
## 13 2256 2256 C8H14ClN5 215.68 CCNC1=N… CCNC1=NC(=NC(=N1)…
## 14 2257 2257 C22H20N3O9+3 470.4 C1=CC(=… C1=CC(=C(C=C1C(=C…
## 15 2258 2258 C22H17N3O9 467.4 C1=CC(=… C1=CC(=C(C=C1C(=C…
## 16 2259 2259 C22H14O9 422.3 C1=CC(=… C1=CC(=C(C=C1C(=C…
## 17 2260 2260 C44H62N2O12 811.0 CCC(C(=… CCC(C(=O)NCC=CC=C…
## # ℹ 35 more variables: InChI <chr>, InChIKey <chr>, IUPACName <chr>,
## # XLogP <dbl>, ExactMass <chr>, MonoisotopicMass <chr>, TPSA <dbl>,
## # Complexity <dbl>, Charge <dbl>, HBondDonorCount <dbl>,
## # HBondAcceptorCount <dbl>, RotatableBondCount <dbl>, HeavyAtomCount <dbl>,
## # IsotopeAtomCount <dbl>, AtomStereoCount <dbl>,
## # DefinedAtomStereoCount <dbl>, UndefinedAtomStereoCount <dbl>,
## # BondStereoCount <dbl>, DefinedBondStereoCount <dbl>, …Download SDF Data
The get_sdf function is designed specifically for
downloading chemical structure data in the widely recognized Structure
Data File (SDF) format. This format is essential in the exchange of
chemical structure information, encompassing comprehensive details such
as molecular structure, associated properties, and extended chemical
data. The primary function of get_sdf is to facilitate the
retrieval of SDF files for specific compounds using their unique CID
from the PubChem database.
To obtain an SDF file for a particular compound, users can execute
the get_sdf function with the compound’s CID as the
identifier. For instance, to download the SDF file for a compound with
CID 2244, the following code is used:
get_sdf(identifier = 2244, namespace = "cid") This code triggers the download of the SDF file for the specified
compound, saving it in a temporary folder with a unique, time-stamped
file name. Furthermore, users can define the path and
file_name arguments to customize the download.
Download Data with Different Formats
The download function simplifies the process of
accessing and downloading content from the PubChem database. This
function is especially significant for researchers who need to retrieve
various types of chemical data in different formats for their work. At
its core, the download function serves to fetch data from
the PubChem database using a specified identifier, and then save this
data in a chosen format to a user-defined location on the local file
system. The function supports a wide range of output formats, including
JSON, XML, SDF and PNG.
For example, to download a JSON file for the compound
“aspirin” and save it to a folder named “Compound” in the current
directory, one would use the following code:
download( filename = "Aspirin", outformat = "json", path = tempdir(),
identifier = "aspirin", namespace = "name", domain = "compound", overwrite = TRUE)This flexibility in specifying parameters makes the
download function particularly useful for diverse research
requirements, from simple data retrievals to complex queries.
Accessing and Exploring Chemical Data with PUG REST Service
The get_pug_rest function is designed to provide easy
and efficient access to the extensive chemical data in PubChem. This
function highlights the advanced capabilities of the Power User Gateway
(PUG) REST service provided by PubChem (Kim et
al. 2015, 2018). It stands out for users who require programmatic
interaction with PubChem’s extensive database, simplifying the otherwise
complex process of data retrieval and analysis. By leveraging the PUG
REST service, get_pug_rest provides a direct and efficient
pathway for accessing a vast array of chemical data, making it an
important resource for researchers in various fields who rely on
accurate and extensive chemical information for their work. This
function is essential for modern computational chemistry, providing
access to big data and efficient data processing, which are crucial for
advancing research and development in the chemical sciences.
PUG REST is a simple way to access PubChem’s data and services, designed for use in scripts, web page JavaScript, and third-party applications. This interface is a simpler and more user-friendly alternative to the complex XML and SOAP envelopes used by other PUG versions. Its design is based on the PubChem identifier system, which includes SID for substances, CID for compounds, and AID for assays, making targeted data retrieval easier. The request architecture in PUG REST is logically segmented into three core components: input (identifiers), operation (actions on identifiers), and output (the format on the PubChem API side).
Overall, the get_pug_rest function meets various user
needs with different input methods, operations, and outputs, allowing
users to customize their queries to the PubChem database in numerous
ways.
1. Retrieving Chemical Structure Information: Users
can request detailed information about chemical structures using
different identifiers like SIDs, CIDs, or common names. The
get_pug_rest function provides a way to access this
information efficiently through PubChem’s PUG REST service. For
instance, the following R code demonstrates how to retrieve chemical
structure information using an SID:
chemical_structure <- get_pug_rest(identifier = "10000",
namespace = "sid",
domain = "substance",
output = "JSON")
chemical_structure##
## An object of class 'PugRestInstance'
##
## Request Details:
## - Domain: Substance
## - Namespace: SID
## - Operation: <NULL>
## - Identifier: 10000
##
## NOTE: Run getter function 'pubChemData(...)' to extract raw data retrieved from PubChem Database.
## See ?pubChemData for details.This code requests detailed information about the chemical structure
associated with the SID “10000”, which corresponds to the compound
dihydroergotamine. The get_pug_rest function sends a query
to the PubChem database, specifying the identifier type (namespace), the
domain of interest, and the output format for the response from the
PubChem API. The result, stored in the chemical_structure object,
contains comprehensive data about the substance, including its
structural details, which can then be utilized for further analysis or
research.
Upon execution, the chemical_structure_result object contains
detailed information about the substance with SID 10000. Now, we use
pubChemData function to access the chemical structure
data.
chemical_structure_result <- pubChemData(chemical_structure)The output is a nested list structure, and here is a breakdown of the key components:
SID and Version: The identifier (SID) and its version are provided:
chemical_structure_result$PC_Substances[[1]]$sid## id version
## 10000 7This indicates that the substance has SID 10000 and is on version 7.
Source Database: Information about the source database is included:
chemical_structure_result$PC_Substances[[1]]$source$db$name## [1] "KEGG"The substance is sourced from the KEGG database, and its KEGG ID is “C07798”:
chemical_structure_result$PC_Substances[[1]]$source$db$source_id## str
## "C07798"Synonyms: Various synonyms for the substance are listed:
chemical_structure_result$PC_Substances[[1]]$synonyms## [1] "511-12-6" "C07798" "Dihydroergotamine"These include its CAS (Chemical Abstracts Service) number “511-12-6”, its KEGG ID “C07798”, and its common name “Dihydroergotamine”.
Comments: Additional comments provide links to related substances:
chemical_structure_result$PC_Substances[[1]]$comment## [1] "Same as: <a href=\"http://pubchem.ncbi.nlm.nih.gov/summary/summary.cgi?sid=96024534\">D07837</a>"This indicates that SID 10000 is the same as another substance with SID 96024534.
Cross-references (Xrefs): Cross-references to other identifiers and databases:
chemical_structure_result$PC_Substances[[1]]$xref## [[1]]
## regid
## "C07798"
##
## [[2]]
## rn
## "511-12-6"
##
## [[3]]
## dburl
## "http://www.genome.jp/kegg/"
##
## [[4]]
## sburl
## "http://www.genome.jp/dbget-bin/www_bget?cpd:C07798"This includes references to the KEGG and its associated URLs.
Compound Information: The compound section details atomic and bonding information. The atoms and their elements:
chemical_structure_result$PC_Substances[[1]]$compound[[1]]$atoms$aid## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46chemical_structure_result$PC_Substances[[1]]$compound[[1]]$atoms$element## [1] 8 8 8 8 8 7 7 7 7 7 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [39] 6 6 6 6 6 1 1 1Atoms are identified by their atomic number, indicating elements like oxygen (8), nitrogen (7), carbon (6), and hydrogen (1).
Bonding information includes atom pairs and bond order:
chemical_structure_result$PC_Substances[[1]]$compound[[1]]$bonds$aid1## [1] 1 1 2 3 4 5 6 6 6 7 7 7 8 8 9 9 9 10 10 11 12 12 13 13 15
## [26] 15 17 17 17 17 18 18 19 20 20 21 22 22 23 23 23 26 28 28 31 33 35 35 36 39
## [51] 40 41 42chemical_structure_result$PC_Substances[[1]]$compound[[1]]$bonds$aid2## [1] 11 13 11 14 16 27 11 14 15 12 16 21 13 27 18 32 38 31 34 12 19 44 14 30 16
## [26] 26 18 20 25 45 29 46 24 22 33 24 28 31 25 27 32 35 29 34 36 37 39 40 37 41
## [51] 42 43 43chemical_structure_result$PC_Substances[[1]]$compound[[1]]$bonds$order## [1] 1 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2
## [39] 1 1 1 1 1 2 1 1 2 1 2 1 2 2 1Coordinates: The coordinates of the atoms provide spatial information about the structure:
chemical_structure_result$PC_Substances[[1]]$compound[[1]]$coords[[1]]$conformers## [[1]]
## [[1]]$x
## [1] 27.2047 28.3501 25.1187 31.8384 21.7294 28.4024 30.6466 24.3766 24.0201
## [10] 19.4272 28.3618 29.5069 26.0652 26.0652 29.5419 30.6873 21.7236 22.8806
## [19] 29.5069 20.5784 31.7917 20.5784 22.8806 31.7977 21.7236 29.5361 22.8806
## [28] 21.7178 22.8689 26.0595 19.4272 24.0201 19.4330 21.7178 30.6699 18.2878
## [37] 18.2878 25.1712 30.6582 31.8150 31.7975 32.9487 32.9428 29.4778 20.5667
## [46] 24.1194
##
## [[1]]$y
## [1] -12.4990 -11.8386 -15.4207 -15.1577 -12.4814 -14.4798 -13.1652 -12.4230
## [9] -16.4548 -20.4226 -13.1593 -12.4990 -13.1593 -14.4857 -15.1460 -14.4915
## [17] -16.4548 -17.1210 -11.1785 -17.1095 -12.5107 -18.4418 -14.4740 -11.1843
## [25] -15.1285 -16.4665 -13.1418 -19.1019 -18.4535 -11.7451 -19.1019 -15.1285
## [33] -16.4492 -20.4226 -17.1327 -18.4418 -17.1095 -17.1210 -18.4535 -16.4782
## [41] -19.1196 -17.1444 -18.4649 -13.8253 -15.7888 -17.9275
##
## [[1]]$style
## [[1]]$style$annotation
## [1] 6 5 5 6 6 5 5
##
## [[1]]$style$aid1
## [1] 11 12 13 15 17 18 23
##
## [[1]]$style$aid2
## [1] 2 44 30 26 45 46 27These coordinates allow for visualization and further spatial analysis of the chemical structure.
2. Performing Structure Searches: The function facilitates searches like substructure or similarity searches and faster synchronous searches for identity, similarity, substructure, and superstructure. These faster searches typically return results in a single call, significantly improving efficiency for users who require quick access to chemical structure data.
For example, to perform a fast identity search, the following R code is used:
structure_search <- get_pug_rest(identifier = "5793",
namespace = "cid",
domain = "compound",
operation = "cids",
searchtype = "fastidentity",
options = list(identity_type = "same_connectivity"),
output = "JSON")
structure_search##
## An object of class 'PugRestInstance'
##
## Request Details:
## - Domain: Compound
## - Namespace: CID
## - Operation: cids
## - Identifier: 5793
##
## NOTE: Run getter function 'pubChemData(...)' to extract raw data retrieved from PubChem Database.
## See ?pubChemData for details.This code initiates a search using the CID “5793” to find all compounds with the same connectivity. The search results are returned in list object.
The output from this search provides a list of CIDs that match the criteria:
structure_search_result <- pubChemData(structure_search)
length(structure_search_result$IdentifierList$CID)## [1] 363head(structure_search_result$IdentifierList$CID)## [1] 5793 206 6036 18950 64689 66371The output contains a comprehensive list of CIDs that have the same connectivity as the original compound with CID 5793, referring to acetaminophen, a commonly used analgesic and antipyretic agent. This indicates that all these compounds have the same atomic connectivity but might differ in other aspects such as stereochemistry or charge states. This fast identity search is particularly useful for researchers looking to quickly find all compounds with the same basic structure, which can then be further analyzed for properties, activities, or potential as drug candidates.
3. Accessing BioAssay Data: The function provides a gateway to comprehensive BioAssay records, including detailed descriptions, datasets, concise readouts, and target information. This is particularly useful for researchers who need to analyze biological activities of compounds.
For example, to retrieve concise data such as the active concentration readout, the following R code can be used:
bioassay_data <- get_pug_rest(identifier = "504526",
namespace = "aid",
domain = "assay",
operation = "concise",
output = "CSV")
bioassay_data##
## An object of class 'PugRestInstance'
##
## Request Details:
## - Domain: Assay
## - Namespace: AID
## - Operation: concise
## - Identifier: 504526
##
## NOTE: Run getter function 'pubChemData(...)' to extract raw data retrieved from PubChem Database.
## See ?pubChemData for details.The output provides a detailed dataset which includes the Activity Outcome, Target Accession, and other relevant information for each assay result.
bioassay_data_result <- pubChemData(bioassay_data)
head(bioassay_data_result)## AID SID CID Activity.Outcome Target.Accession Target.GeneID
## 1 504526 103061373 6619281 Active NA NA
## 2 504526 103904139 2971528 Active NA NA
## 3 504526 103904144 4969604 Inactive NA NA
## 4 504526 104169543 49842897 Active NA NA
## 5 504526 104169544 49842896 Active NA NA
## 6 504526 104169545 1077725 Inactive NA NA
## Activity.Value..uM. Activity.Name
## 1 3.6 IC50
## 2 31.4 IC50
## 3 50.0 IC50
## 4 5.1 IC50
## 5 34.6 IC50
## 6 50.0 IC50
## Assay.Name
## 1 A Cell Based HTS Approach for the Discovery of New Inhibitors of Respiratory syncytial virus (RSV) using synthesized compounds (6)
## 2 A Cell Based HTS Approach for the Discovery of New Inhibitors of Respiratory syncytial virus (RSV) using synthesized compounds (6)
## 3 A Cell Based HTS Approach for the Discovery of New Inhibitors of Respiratory syncytial virus (RSV) using synthesized compounds (6)
## 4 A Cell Based HTS Approach for the Discovery of New Inhibitors of Respiratory syncytial virus (RSV) using synthesized compounds (6)
## 5 A Cell Based HTS Approach for the Discovery of New Inhibitors of Respiratory syncytial virus (RSV) using synthesized compounds (6)
## 6 A Cell Based HTS Approach for the Discovery of New Inhibitors of Respiratory syncytial virus (RSV) using synthesized compounds (6)
## Assay.Type PubMed.ID RNAi
## 1 Confirmatory NA NA
## 2 Confirmatory NA NA
## 3 Confirmatory NA NA
## 4 Confirmatory NA NA
## 5 Confirmatory NA NA
## 6 Confirmatory NA NAThe bioassay_data dataframe contains several columns, each providing specific information about the assay results. For example:
AID (Assay Identifier): This column contains the unique identifier for the assay, which in this case is “504526” for all rows, indicating that all data pertains to the same assay.
SID (Substance Identifier) and CID (Compound Identifier): These columns list the identifiers for the substances and compounds tested in the assay.
Activity.Outcome: This column indicates whether the substance or compound was found to be “Active” or “Inactive” in the assay.
Target.Accession: This column would typically contain the accession numbers for the protein targets involved in the assay. An accession number is a unique identifier assigned to a protein sequence record. In the given dataset, all entries show “NA”, indicating that specific target accession numbers are not provided for these assay results.
Target.GeneID: This column would contain the GeneID, a unique identifier for genes provided by the NCBI Gene database. Similar to the Target.Accession column, all entries in this dataset show “NA”, suggesting that no specific gene identifiers are associated with these assay results.
Activity.Value..uM.: This column shows the concentration at which the activity was measured, usually given in micromolar (uM). For example, values range from 0.22 uM to 50.00 uM, with “IC50” indicating the concentration at which 50% inhibition is observed.
Activity.Name: This column typically indicates the type of activity measured, in this case, “IC50” for inhibitory concentration.
Assay.Name: This column provides a detailed description of the assay. For example, all entries describe a cell-based high-throughput screening (HTS) approach for discovering new inhibitors of Respiratory Syncytial Virus (RSV) using synthesized compounds.
Assay.Type: This column indicates the type of assay, with all entries marked as “Confirmatory,” suggesting these are follow-up tests to initial screenings.
PubMed.ID and RNAi: These columns are included but contain “NA,” indicating no specific PubMed reference or RNA interference information is provided for these entries.
The concise format of this dataset allows researchers to quickly assess the activity outcomes of various substances and compounds tested in the assay. For instance, the dataset shows that compounds with SIDs 103061373, 103904139, and others are active against RSV at specific concentrations, while others are inactive, providing valuable insights into potential therapeutic candidates.
4. Gene and Protein Data Retrieval: To retrieve gene
and protein data, the get_pug_rest function can be employed
to access detailed information about genes and proteins, which is
crucial for genetic and molecular biology research. This can be done by
retrieving concise bioactivity data for a specific gene or protein,
using gene IDs or protein accession numbers.
Here is an example of how to retrieve concise bioactivity data for a specific gene:
geneData <- get_pug_rest(identifier = "13649",
namespace = "geneid",
domain = "gene",
operation = "concise",
output = "CSV")
geneData##
## An object of class 'PugRestInstance'
##
## Request Details:
## - Domain: Gene
## - Namespace: DomainSpecific
## - Operation: concise
## - Identifier: 13649
##
## NOTE: Run getter function 'pubChemData(...)' to extract raw data retrieved from PubChem Database.
## See ?pubChemData for details.The output for this function provides a dataframe with information on bioactivity data related to the gene ID “13649”. This gene ID corresponds to the epidermal growth factor receptor (EGFR) gene in mice. This gene encodes a transmembrane glycoprotein that is a member of the protein kinase superfamily. The encoded protein is a receptor for members of the epidermal growth factor family. Mutations, amplifications, or misregulations of EGFR or family members are implicated in various cancers. (ATIF)
geneData_result <- pubChemData(geneData)
head(geneData_result)## AID SID CID Activity.Outcome Target.Accession
## 1 66438 103250953 25017867 Active Q01279
## 2 66438 103432098 454217 Active Q01279
## 3 69721 103358917 135512509 Unspecified Q01279
## 4 69721 103358918 135434086 Unspecified Q01279
## 5 69721 103358919 135455949 Unspecified Q01279
## 6 69722 103253186 5328592 Unspecified Q01279
## Activity.Name Activity.Qualifier Activity.Value..uM.
## 1 Effective concentration = 0.01
## 2 Effective concentration = 0.22
## 3 IC50 = 22.70
## 4 IC50 = 36.90
## 5 IC50 = 11.30
## 6 IC50 > 100.00
## Assay.Name
## 1 Inhibition of epidermal growth factor binding in C3H10T1/2 cells
## 2 Inhibition of epidermal growth factor binding in C3H10T1/2 cells
## 3 Inhibition of Epidermal growth factor receptor mediated mitogenesis of NIH3T3 cells
## 4 Inhibition of Epidermal growth factor receptor mediated mitogenesis of NIH3T3 cells
## 5 Inhibition of Epidermal growth factor receptor mediated mitogenesis of NIH3T3 cells
## 6 Inhibition of epidermal growth factor receptor (EGFR-mediated tyrosine autophosphorylation in mouse fibroblasts.
## Assay.Type PubMed.ID RNAi
## 1 Confirmatory 1597853 NA
## 2 Confirmatory 1597853 NA
## 3 Confirmatory 9748366 NA
## 4 Confirmatory 9748366 NA
## 5 Confirmatory 9748366 NA
## 6 Confirmatory 8027985 NAThis data provides researchers with detailed insights into the bioactivity of different substances tested in relation to a specific gene. The availability of target accession numbers and detailed assay descriptions makes it easier to understand the context and significance of each entry in the dataset. This information is crucial for advancing genetic and molecular biology research by understanding the effects of various substances on specific genes and proteins.
To retrieve concise bioactivity data for the specified protein with the accession number “Q01279” (which corresponds to the EGFR in mice), use the following R code:
protein_data <- get_pug_rest(identifier = "Q01279",
namespace = "accession",
domain = "protein",
operation = "concise",
output = "CSV")
protein_data##
## An object of class 'PugRestInstance'
##
## Request Details:
## - Domain: Protein
## - Namespace: DomainSpecific
## - Operation: concise
## - Identifier: Q01279
##
## NOTE: Run getter function 'pubChemData(...)' to extract raw data retrieved from PubChem Database.
## See ?pubChemData for details.protein_data_result <- pubChemData(protein_data)
head(protein_data_result)## AID SID CID Activity.Outcome Target.GeneID
## 1 66438 103250953 25017867 Active 13649
## 2 66438 103432098 454217 Active 13649
## 3 69721 103358917 135512509 Unspecified 13649
## 4 69721 103358918 135434086 Unspecified 13649
## 5 69721 103358919 135455949 Unspecified 13649
## 6 69722 103253186 5328592 Unspecified 13649
## Activity.Name Activity.Qualifier Activity.Value..uM.
## 1 Effective concentration = 0.01
## 2 Effective concentration = 0.22
## 3 IC50 = 22.70
## 4 IC50 = 36.90
## 5 IC50 = 11.30
## 6 IC50 > 100.00
## Assay.Name
## 1 Inhibition of epidermal growth factor binding in C3H10T1/2 cells
## 2 Inhibition of epidermal growth factor binding in C3H10T1/2 cells
## 3 Inhibition of Epidermal growth factor receptor mediated mitogenesis of NIH3T3 cells
## 4 Inhibition of Epidermal growth factor receptor mediated mitogenesis of NIH3T3 cells
## 5 Inhibition of Epidermal growth factor receptor mediated mitogenesis of NIH3T3 cells
## 6 Inhibition of epidermal growth factor receptor (EGFR-mediated tyrosine autophosphorylation in mouse fibroblasts.
## Assay.Type PubMed.ID
## 1 Confirmatory 1597853
## 2 Confirmatory 1597853
## 3 Confirmatory 9748366
## 4 Confirmatory 9748366
## 5 Confirmatory 9748366
## 6 Confirmatory 8027985This data provides researchers with detailed insights into the bioactivity of different substances tested in relation to a specific protein. The availability of target gene IDs and assay information makes it easier to understand the context and significance of each entry in the dataset.
5. Pathway Information: The function offers access to detailed pathway information, essential for bioinformatics and molecular biology research. It provides a list of pathways involving a specific protein. For example, P00533 is the accession number for the human EGFR, which is a protein involved in the regulation of cell growth, survival, proliferation, and differentiation.
To retrieve pathway information for protein accession P00533:
pathway_data <- get_pug_rest(identifier = "P00533",
namespace = "accession",
domain = "protein",
operation = "pwaccs",
output = "JSON")
pathway_data##
## An object of class 'PugRestInstance'
##
## Request Details:
## - Domain: Protein
## - Namespace: DomainSpecific
## - Operation: pwaccs
## - Identifier: P00533
##
## NOTE: Run getter function 'pubChemData(...)' to extract raw data retrieved from PubChem Database.
## See ?pubChemData for details.The output provides a list of pathways:
pathway_data_result <- pubChemData(pathway_data)
head(pathway_data_result$InformationList$Information[[1]]$PathwayAccession)## [1] "PathBank:SMP0000472" "PathBank:SMP0000473" "PathBank:SMP0000474"
## [4] "PathBank:SMP0000475" "PathBank:SMP0000476" "PathBank:SMP0063810"This information provides a comprehensive list of pathways involving the specified protein, which is essential for understanding its biological roles and interactions. Each pathway entry includes the database source and the specific pathway identifier.
6. Taxonomy Data: The function enables researchers to access detailed taxonomy information, aiding in biological and environmental research. It provides summaries of taxonomy data for given taxonomic identifiers. For example, Taxonomy ID 2697049 corresponds to Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2):
taxonomy_data <- get_pug_rest(identifier = c("2697049"),
namespace = "taxid",
domain = "taxonomy",
operation = "summary",
output = "JSON")
taxonomy_data##
## An object of class 'PugRestInstance'
##
## Request Details:
## - Domain: Taxonomy
## - Namespace: DomainSpecific
## - Operation: summary
## - Identifier: 2697049
##
## NOTE: Run getter function 'pubChemData(...)' to extract raw data retrieved from PubChem Database.
## See ?pubChemData for details.The output provides detailed taxonomy summaries:
taxonomy_data_result <- pubChemData(taxonomy_data)
taxonomy_data_result$TaxonomySummaries$TaxonomySummary## [[1]]
## [[1]]$TaxonomyID
## [1] 2697049
##
## [[1]]$ScientificName
## [1] "Severe acute respiratory syndrome coronavirus 2"
##
## [[1]]$CommonName
## [1] ""
##
## [[1]]$Rank
## [1] "no rank"
##
## [[1]]$RankedLineage
## Species
## "Severe acute respiratory syndrome-related coronavirus"
## Genus
## "Betacoronavirus"
## Family
## "Coronaviridae"
## Order
## "Nidovirales"
## Class
## "Pisoniviricetes"
## Phylum
## "Pisuviricota"
## Kingdom
## "Orthornavirae"
## Superkingdom
## "Viruses"
##
## [[1]]$Synonym
## [1] "2019-nCoV"
## [2] "COVID-19 virus"
## [3] "HCoV-19"
## [4] "Human coronavirus 2019"
## [5] "SARS-2"
## [6] "SARS2"
## [7] "SARS-CoV2"
## [8] "Severe acute respiratory syndrome coronavirus 2"This information includes taxonomy ID, scientific name, common name, ranks, ranked lineages, and synonyms for the specified taxa. This detailed taxonomy data is essential for various research applications, including evolutionary studies and disease research.
7. Cell Line Information: The function is also useful in accessing detailed information about various cell lines, vital for cellular biology and pharmacology research. For example, CHEMBL3308376 corresponds to the HeLa cell line:
cell_line_data <- get_pug_rest(identifier = c("CHEMBL3308376"),
namespace = "cellacc",
domain = "cell",
operation = "summary",
output = "JSON")
cell_line_data##
## An object of class 'PugRestInstance'
##
## Request Details:
## - Domain: Cell
## - Namespace: DomainSpecific
## - Operation: summary
## - Identifier: CHEMBL3308376
##
## NOTE: Run getter function 'pubChemData(...)' to extract raw data retrieved from PubChem Database.
## See ?pubChemData for details.The output provides detailed cell line summaries:
cell_line_data_result <- pubChemData(cell_line_data)
cell_line_data_result$CellSummaries$CellSummary## [[1]]
## [[1]]$CellAccession
## [1] "CVCL_0030"
##
## [[1]]$Name
## [1] "HeLa"
##
## [[1]]$Sex
## [1] "Female"
##
## [[1]]$Category
## [1] "Cancer cell line"
##
## [[1]]$SourceTissue
## [1] "uterine cervix"
##
## [[1]]$SourceTaxonomyID
## [1] 9606
##
## [[1]]$SourceOrganism
## [1] "Homo sapiens (human)"
##
## [[1]]$Synonym
## [1] "HELA" "Hela" "He La"
## [4] "He-La" "HeLa-CCL2" "Henrietta Lacks cells"
## [7] "Helacyton gartleri"This information includes cell accession number, name, sex, category, source tissue, source taxonomy ID, source organism, and synonyms for the specified cell lines.
Enhancing Chemical Data Access with PUG View Service
The get_pug_view function is designed to provide access
to detailed summary reports and additional information not typically
found in primary PubChem Substance, Compound, or BioAssay records. It
utilizes the PUG View service, a REST-style web service of PubChem (Kim et al. 2019), to generate comprehensive
reports for individual PubChem records (Figure 2). The primary aim of
get_pug_view is to offer a different approach from the PUG
REST service, focusing on delivering complete summary reports rather
than smaller bits of information. This function supports various data
formats and record types, making it a versatile tool for users needing
comprehensive information from the PubChem database. Key aspects
include:
Flexible Data Retrieval: Users can choose between obtaining an index (summary or table of contents) or full data retrieval, catering to both overview and detailed information requirements. This flexibility allows users to access just the right amount of data they need for their specific research purposes.
Diverse Record Types: The function is capable of accessing a wide range of records, including compounds, substances, bioassays, patents, genes, proteins, pathways, taxonomies, and cell lines. This broad capability ensures that users can retrieve comprehensive data across various scientific domains using their respective identifiers or names.
Annotations and Detailed Information: The
get_pug_view function can retrieve specific types of
information, such as experimental properties, safety and hazard
labeling, and more. This feature is particularly valuable for users
needing in-depth annotations and detailed descriptions across PubChem’s
extensive databases.
Comprehensive Reports: It provides detailed summaries that encompass chemical properties, biological activities, safety information, patents, and literature references. This comprehensive reporting is crucial for researchers who require a holistic view of PubChem records for their studies and analyses.
In summary, the get_pug_view function offers in-depth
and comprehensive reports that facilitate advanced research and
development activities. By leveraging the PUG View service, it enables
efficient access to detailed and annotated data, enhancing the user’s
ability to make informed decisions based on extensive PubChem
records.
Using the PubChemR package to access PubChem’s PUG-View database, with queries in URL syntax serving as function arguments
The get_pug_view function finds its application in
various scenarios, making it a crucial resource in chemical data
analysis:
1. Full Data Record: For researchers requiring
comprehensive data of compounds, substances, or bioassays,
get_pug_view provides detailed reports including
experimental properties, safety information, and more.
We will initialize the retrieval of comprehensive data for the compound with ID 2244 (Aspirin) from PubChem using the following code chunk:
full_record_2244 <- get_pug_view(annotation = "data",
identifier = "2244",
domain = "compound",
output = "JSON")
full_record_2244##
## PUG View Data from PubChem Database
##
## Request Details:
## - Domain: Compound
## - Annotation: data
## - Identifier: 2244
##
## Pug View Details:
## - RecordType (1): [<unnamed character>]
## - RecordNumber (1): [<unnamed numeric>]
## - RecordTitle (1): [<unnamed character>]
## - Section (21): [<unnamed list>] Structures, Primary Hazards, ... and 19 more.
## - Reference (274): [<unnamed list>]
##
## NOTE: Run getter function 'retrieve()' with element name above to extract data from corresponding list.
## See ?retrieve for details.The resulting object contains extensive information about the compound, including various sections and references.
We can extract the record type, record number, and record title to
confirm that the record pertains to a chemical compound, to identify the
specific compound ID for Aspirin, and to verify that Aspirin is the
common name for the compound with ID 2244. Moreoveer,
retrieve function can be used to show detailed information
about the references, including reference number, source name, source
ID, name, description, URL, and license URL. Each reference provides
insights into various aspects related to the compound Aspirin, sourced
from databases like the Australian Industrial Chemicals Introduction
Scheme (AICIS), CAMEO Chemicals, CAS Common Chemistry, and others. These
references include descriptions of the chemical properties, regulatory
information, safety data, and links to the original sources for further
details.
To access the specific sections within the retrieved data, we use the following code:
sections <- retrieve(object = full_record_2244, .slot = "Section")
sections##
## PUG View Data Sections
##
## Request Details:
## - Record Type: CID
## - Record Number: 2244
## - Record Title: Aspirin
##
## Section Details:
## - Number of available sections: 21
## - Section headings: Structures, Primary Hazards, ... and 19 more.
##
## NOTE: Run getter function 'section()' to extract section data. To list available sections, run 'sectionList()'.
## See ?section and ?sectionList for details.This code retrieves the sections available within the full record of Aspirin. The output indicates that there are 20 sections available, with headings such as Structures, Chemical Safety, and others.
Now, we can employ the sectionList function to display
all sections available in the “sections” object:
sectionList(object = sections)## # A tibble: 21 × 2
## SectionID Headings
## <chr> <chr>
## 1 S1 Structures
## 2 S2 Primary Hazards
## 3 S3 Names and Identifiers
## 4 S4 Chemical and Physical Properties
## 5 S5 Spectral Information
## 6 S6 Related Records
## 7 S7 Chemical Vendors
## 8 S8 Drug and Medication Information
## 9 S9 Minerals
## 10 S10 Pharmacology and Biochemistry
## # ℹ 11 more rowsThe “SectionID” column contains unique identifiers for each section, labeled S1 through S20. The “Headings” column provides descriptive titles for these sections, indicating the type of information contained within each.
The sections listed are:
1. Structures: Details on the structural information of Aspirin.
2. Chemical Safety: Information related to the safety measures and regulations for handling Aspirin.
3. Names and Identifiers: Various names and identifiers associated with Aspirin.
4. Chemical and Physical Properties: Data on the chemical and physical properties of Aspirin.
5. Spectral Information: Spectral data related to Aspirin.
6. Related Records: Records related to Aspirin.
7. Chemical Vendors: Information about vendors that supply Aspirin.
8. Drug and Medication Information: Details on the use of Aspirin as a drug or medication.
9. Pharmacology and Biochemistry: Information on the pharmacological and biochemical properties of Aspirin.
10. Use and Manufacturing: Data on the use and manufacturing processes of Aspirin.
11. Identification: Identification information for Aspirin.
12. Safety and Hazards: Safety hazards associated with Aspirin.
13. Toxicity: Toxicological information about Aspirin.
14. Associated Disorders and Diseases: Disorders and diseases associated with Aspirin.
15. Literature: References to literature involving Aspirin.
16. Patents: Patent information related to Aspirin.
17. Interactions and Pathways: Biological interactions and pathways involving Aspirin.
18. Biological Test Results: Results from biological tests conducted on Aspirin.
19. Taxonomy: Taxonomic information related to Aspirin.
20. Classification: Classification information for Aspirin.
These sections provide a comprehensive overview of various aspects of Aspirin. Now, we will focus on detailed data from the first section, “Structures,” with the section ID “S1”.
First, we assign the section to an object using the
section function and then examine its contents:
s1 <- section(object = sections, .id = "S1")The output provides an overview of the “Structures” section:
s1##
## PUG View Data Sections (Structures)
##
## Request Details:
## - Record Type: CID
## - Record Number: 2244
## - Record Title: Aspirin
##
## Section Details:
## - TOCHeading (1): [<unnamed character>]
## - Description (1): [<unnamed character>]
## - Section (3): [<unnamed list>] 2D Structure, 3D Conformer, ... and 1 more.
##
## NOTE: Run getter function 'retrieve()' with element name above to extract data from corresponding list.
## See ?retrieve for details.
##
## NOTE: Run getter function 'section()' to extract section data. To list available sections, run 'sectionList()'.
## See ?section and ?sectionList for details.Next, we list the sub-sections within the “Structures” section. The output shows the available sub-sections:
sectionList(object = s1)## # A tibble: 3 × 2
## SectionID Headings
## <chr> <chr>
## 1 S1 2D Structure
## 2 S2 3D Conformer
## 3 S3 Crystal StructuresThis breakdown allows us to see that the “Structures” section contains detailed depictions of Aspirin, including 2D structures, 3D conformers, and crystal structures. Each sub-section can be further explored to gain more specific information about the molecular structure of Aspirin.
2. Accessing Specific Headings: Users can retrieve data under specific headings for targeted information, such as boiling points or viscosity measurements.
First, we initiate the retrieval of data for a specific heading using
the get_pug_view function, focusing on the “Boiling Point”
heading within the “heading” domain.
specific_headings <- get_pug_view(annotation = "annotations",
identifier = "Boiling Point",
domain = "heading",
output = "JSON",
headingType = "Compound")
specific_headings##
## PUG View Data from PubChem Database
##
## Request Details:
## - Domain: DomainSpecific (heading)
## - Annotation: annotations
## - Identifier: Boiling%20Point
##
## Pug View Details:
## - Annotation (1000): [<unnamed list>]
## - Page (1): [<unnamed numeric>]
## - TotalPages (1): [<unnamed numeric>]
##
## NOTE: Run getter function 'retrieve()' with element name above to extract data from corresponding list.
## See ?retrieve for details.The function call returns data from the PubChem database, specifying details about the request, including the domain, annotation type, identifier, and the number of annotations retrieved. It highlights that 1000 annotations were fetched, spread across multiple pages.
Next, we utilize the retrieve function to extract the
“Annotation” slot from the specific_headings object. By
displaying the annotations, we gain insight into the structure and
content of the data. Each annotation includes the source name, source
ID, compound name, description, URLs, and detailed data related to the
boiling point, complete with references and specific values.
annotation <- retrieve(specific_headings, .slot = "Annotation", .to.data.frame = FALSE)
annotation[[1]]## $SourceName
## [1] "Hazardous Substances Data Bank (HSDB)"
##
## $SourceID
## [1] "30"
##
## $Name
## [1] "NITROGLYCERIN"
##
## $Description
## [1] "The Hazardous Substances Data Bank (HSDB) is a toxicology database that focuses on the toxicology of potentially hazardous chemicals. It provides information on human exposure, industrial hygiene, emergency handling procedures, environmental fate, regulatory requirements, nanomaterials, and related areas. The information in HSDB has been assessed by a Scientific Review Panel."
##
## $URL
## [1] "https://pubchem.ncbi.nlm.nih.gov/source/hsdb/30"
##
## $LicenseURL
## [1] "https://www.nlm.nih.gov/web_policies.html"
##
## $Data
## $Data[[1]]
## $Data[[1]]$TOCHeading
## type #TOCHeading
## "Compound" "Boiling Point"
##
## $Data[[1]]$Description
## [1] "PEER REVIEWED"
##
## $Data[[1]]$Reference
## [1] "O'Neil, M.J. (ed.). The Merck Index - An Encyclopedia of Chemicals, Drugs, and Biologicals. 13th Edition, Whitehouse Station, NJ: Merck and Co., Inc., 2001., p. 1185"
##
## $Data[[1]]$ExtendedReference
## $Data[[1]]$ExtendedReference[[1]]
## $Data[[1]]$ExtendedReference[[1]]$Citation
## [1] "O'Neil, M.J. (ed.). The Merck Index - An Encyclopedia of Chemicals, Drugs, and Biologicals. 13th Edition, Whitehouse Station, NJ: Merck and Co., Inc., 2001., p. 1185"
##
## $Data[[1]]$ExtendedReference[[1]]$Matched
## $Data[[1]]$ExtendedReference[[1]]$Matched$PCLID
## [1] 900133450
##
## $Data[[1]]$ExtendedReference[[1]]$Matched$Citation
## [1] "O'Neil, M.J. (ed.). The Merck Index - An Encyclopedia of Chemicals, Drugs, and Biologicals. 13th Edition, Whitehouse Station, NJ: Merck and Co., Inc., 2001., p. 1185"
##
##
##
##
## $Data[[1]]$Value
## $Data[[1]]$Value$StringWithMarkup
## $Data[[1]]$Value$StringWithMarkup[[1]]
## String
## "Explodes at 218 °C"
##
##
##
##
## $Data[[2]]
## $Data[[2]]$TOCHeading
## type #TOCHeading
## "Compound" "Boiling Point"
##
## $Data[[2]]$Description
## [1] "PEER REVIEWED"
##
## $Data[[2]]$Reference
## [1] "Weast, R.C. (ed.) Handbook of Chemistry and Physics. 69th ed. Boca Raton, FL: CRC Press Inc., 1988-1989., p. C-291"
##
## $Data[[2]]$ExtendedReference
## $Data[[2]]$ExtendedReference[[1]]
## $Data[[2]]$ExtendedReference[[1]]$Citation
## [1] "Weast, R.C. (ed.) Handbook of Chemistry and Physics. 69th ed. Boca Raton, FL: CRC Press Inc., 1988-1989., p. C-291"
##
## $Data[[2]]$ExtendedReference[[1]]$Matched
## $Data[[2]]$ExtendedReference[[1]]$Matched$PCLID
## [1] 900017161
##
## $Data[[2]]$ExtendedReference[[1]]$Matched$Citation
## [1] "Weast, R.C. (ed.) Handbook of Chemistry and Physics. 69th ed. Boca Raton, FL: CRC Press Inc., 1988-1989., p. C-291"
##
##
##
##
## $Data[[2]]$Value
## $Data[[2]]$Value$StringWithMarkup
## $Data[[2]]$Value$StringWithMarkup[[1]]
## String
## "BOILING POINT: 125 °C @ 2 MM HG"
##
##
##
##
##
## $ANID
## [1] 2
##
## $LinkedRecords
## $LinkedRecords$CID
## [1] 4510
##
## $LinkedRecords$ConceptID
## [1] 6474This step reveals detailed information about various compounds, including their boiling points. For instance, it might display data such as “NITROGLYCERIN” from the “Hazardous Substances Data Bank (HSDB)” with specific boiling point details like “Explodes at 218 °C.”
3. Literature and Publication Data: The function can be used to retrieve literature associated with a compound, aiding in academic research and publication review.
In the given example, we are querying the PubChem database for literature information related to the compound with identifier “1234” in the compound domain. The identifier “1234” corresponds to the compound Gallopamil. Gallopamil is a pharmaceutical compound used primarily as a calcium channel blocker, which is useful in treating cardiovascular conditions such as angina pectoris and hypertension.
By running the get_pug_view function with the identifier
“1234”, we retrieve various details about Gallopamil, including
references to related literature.
literature <- get_pug_view(annotation = "literature",
identifier = "1234",
domain = "compound",
output = "JSON")
literature##
## PUG View Data from PubChem Database
##
## Request Details:
## - Domain: Compound
## - Annotation: literature
## - Identifier: 1234
##
## Pug View Details:
## - RecordType (1): [<unnamed character>]
## - RecordNumber (1): [<unnamed numeric>]
## - AllURL (1): [<unnamed character>]
## - Subheadings (15): [<unnamed list>]
##
## NOTE: Run getter function 'retrieve()' with element name above to extract data from corresponding list.
## See ?retrieve for details.The following code provides a URL, which directs to the PubMed search page for the specified compound. This link is valuable for researchers seeking detailed literature information related to the compound identified by CID 1234.
retrieve(literature, .slot = "AllURL")## # A tibble: 1 × 1
## value
## <chr>
## 1 https://pubmed.ncbi.nlm.nih.gov/?cmd=search&term=%22Gallopamil%22[Mesh%20Term…4. 3D Protein Structures: The
get_pug_view function allows access to detailed 3D protein
structure information associated with specific compounds. These 3D
structures provide critical insights into the molecular interactions,
mechanisms of action, and potential binding sites of compounds, which
are essential for understanding their biological effects. Access to such
detailed structural data aids in drug design, understanding enzyme
mechanisms, and studying protein-ligand interactions. The visual
representation of these structures, along with associated metadata like
MMDB (Molecular Modeling Database) and PDB (Protein Data Bank) IDs, URLs
for accessing detailed pages, and descriptions of the structures,
further enhances the utility of this function in scientific research and
development.
The following code retrieves a list of 3D protein structures associated with Aspirin (ID:2244):
list_3d_proteins <- get_pug_view(annotation = "structure",
identifier = "2244",
domain = "compound",
output = "JSON")
list_3d_proteins##
## PUG View Data from PubChem Database
##
## Request Details:
## - Domain: Compound
## - Annotation: structure
## - Identifier: 2244
##
## Pug View Details:
## - RecordType (1): [<unnamed character>]
## - RecordNumber (1): [<unnamed numeric>]
## - URL (1): [<unnamed character>]
## - NumberOfStructures (1): [<unnamed numeric>]
## - Structures (8): [<unnamed list>]
##
## NOTE: Run getter function 'retrieve()' with element name above to extract data from corresponding list.
## See ?retrieve for details.This code fetches detailed information about the 3D protein structures related to Aspirin. The output includes several components.
First, the URL for the data is retrieved:
retrieve(list_3d_proteins, .slot = "URL")## # A tibble: 1 × 1
## value
## <chr>
## 1 https://www.ncbi.nlm.nih.gov/sites/entrez?LinkName=pccompound_structure&db=pc…This output provides the URL to the PubChem page that contains detailed information about the 3D structures of Aspirin. This URL can be visited to explore further details visually and interactively.
Next, the number of 3D structures available is retrieved:
retrieve(list_3d_proteins, .slot = "NumberOfStructures")## # A tibble: 1 × 1
## value
## <dbl>
## 1 8The output indicates that there are 8 different 3D structures available for Aspirin.
Finally, details of each structure are retrieved:
list_3d_proteins_structures <- retrieve(list_3d_proteins, .slot = "Structures", .to.data.frame = FALSE)This code outputs a list of details for each 3D structure, including the MMDB ID, PDB ID, URLs for accessing and visualizing the structures, descriptions, and taxonomic information. For example, one of the structures is described as “Cryo-EM structure of aspirin-bound ABCC4,” with the MMDB ID 230639 and PDB ID “8J3W.” The structure is associated with Homo sapiens (human) as indicated by the taxonomy information.
list_3d_proteins_structures[[1]]## $MMDB_ID
## [1] 230639
##
## $PDB_ID
## [1] "8J3W"
##
## $URL
## [1] "https://www.ncbi.nlm.nih.gov/Structure/mmdb/mmdbsrv.cgi?uid=230639"
##
## $ImageURL
## [1] "https://www.ncbi.nlm.nih.gov/Structure/mmdb/mmdbimage.fcgi?small=t&id=230639"
##
## $Description
## [1] "Cryo-EM structure of aspirin-bound ABCC4"
##
## $Taxonomy
## $Taxonomy$ID
## [1] 9606
##
## $Taxonomy$Name
## [1] "Homo sapiens"5. NCBI LinkOut Records: The
get_pug_view function is capable of listing all LinkOut
records for substances, compounds, or assays, which is beneficial for
tracking external resources and databases linked to specific chemical
entities. This functionality is especially useful for researchers who
need to access a wide array of related data from different external
sources.
Here is an example of how the LinkOut records can be retrieved for identifier “1234”:
ncbi_linkouts <- get_pug_view(annotation = "linkout",
identifier = "1234",
domain = "compound",
output = "JSON")Next, the retrieved data can then be accessed as follows:
retrieve(ncbi_linkouts, .slot = "ObjUrl", .to.data.frame = FALSE)## $Url
## [1] "http://partnersolution.ingenuity.com/?cid=97ae3f91eab87a&p1=EntrezPubChem&p2=GV&s=&ipaUri=%2Fpa%2Fapi%2Fv2%2Fgeneview%3Fapplicationname%3DEntrezPubChem%26geneId%3DING:qkb%26geneidtype%3Dingenuity"
##
## $SubjectType
## [1] "molecular interactions"
##
## $Category
## [1] "Chemical Information"
##
## $Attribute
## [1] "subscription/membership/fee required"
##
## $Provider
## Name NameAbbr
## "Ingenuity Pathways Analysis" "Ingenuity"
## Id Url
## "5628" "http://www.ingenuity.com"The extracted data includes information such as the URL of the external resource, the subject type, the category of information, attributes indicating if a subscription or fee is required, and the provider’s details. For instance, the URL “http://partnersolution.ingenuity.com/?cid=97ae3f91eab87a&p1=EntrezPubChem&p2=GV&s=&ipaUri=%2Fpa%2Fapi%2Fv2%2Fgeneview%3Fapplicationname%3DEntrezPubChem%26geneId%3DING:qkb%26geneidtype%3Dingenuity” points to a resource provided by Ingenuity Pathways Analysis, categorized under “Chemical Information” and related to “molecular interactions”. This URL requires a subscription or membership for access.
Such detailed LinkOut records facilitate the exploration of interconnected data across various platforms, enabling researchers to efficiently gather comprehensive information related to their chemical entities of interest.
Discussion
Usage Policy of PUG REST Service
Please note that PUG REST is not intended for handling very large volumes of requests, such as those numbering in the millions. To prevent overloading the PubChem servers, it is requested that any script or application limit the request rate to no more than five requests per second. This measure helps ensure the stability and reliability of the service for all users. For more information on request volume limitations and automated rate limiting (throttling), please refer to PubChem’s dynamic request throttling documentation (https://pubchem.ncbi.nlm.nih.gov/docs/dynamic-request-throttling).
In some cases, a 503 HTTP status code may be returned when the server is temporarily unable to service the request due to maintenance downtime or capacity issues. If this occurs, it is advisable to try the request again later. This status code indicates that the server is currently overloaded or undergoing maintenance, and retrying the request after some time should allow it to be processed successfully.
Furter Research
The primary contribution of is its facilitation of direct access to PubChem’s chemical data through the R programming environment. This functionality addresses a specific need for researchers who rely on R for data analysis and representation, allowing for more straightforward integration of chemical data into their workflows However, the effectiveness of is closely tied to the quality and comprehensiveness of the PubChem database. Inaccuracies or gaps in the PubChem data directly impact the outputs of , which is an important consideration for users relying on this tool for research or data analysis. As PubChem’s database is dynamic and continually updated, keeping synchronized with these updates is critical for maintaining its accuracy and relevance.
While currently focuses on accessing PubChem data and does not include data analysis functions, future enhancements could potentially explore these areas. For instance, more sophisticated data analysis capabilities within the package itself could be developed, enabling users to perform preliminary analysis within the same framework. Another possible direction could be the integration with other chemical databases, expanding the range of accessible data. Additionally, improving the user interface for greater ease of use and better data visualization capabilities could make the tool more accessible to a wider range of users with varying levels of expertise in R programming. These are potential directions for future research and development, although there are no immediate plans to implement these features.
Summary
represents a significant advancement in accessing chemical data through the R programming environment. It offers a straightforward, effective, and easy-to-use way to access information from the PubChem database, improving how users can get chemical data. combines practical features with user-friendly design, making it a useful tool for researchers in various scientific areas. As it continues to receive updates and enhancements, will keep up with changes in chemical data and computational technology.