InChI Tag: English

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Chemistry Programming with Python – Web Scraping Wikipedia For Chemical Identifiers (Tutorial)

Andrew P. Cornell, Robert E. Belford

Chemistry Department, University of Arkansas at Little Rock, Little Rock, Arkansas 72204

 

Abstract

Many individual chemicals have a specific page on Wikipedia that will give information about the use, manufacture and properties of that chemical. The properties that are displayed off to the side include the relevant chemical identifiers along with alternate names and reaction information. There are several different identifier formats displayed within the properties box that include InChI (International Chemical Identifier), SMILES (Simplified Molecular-Input Line-Entry System) and various registration numbers. This lesson will explain how Python can be used to web scrape Wikipedia and retrieve the InChI after the user inputs a chemical name. Web scraping is a process for extracting the contents of a web page. This is often useful for working with online sources that do not offer an API (Application Programming Interface) for certain types of data. Wikipedia does have API’s for a lot of the information published, however this tutorial would like to look at the technique of web scraping with Python as an alternate method.

This program will work by importing a few helper modules that will allow the Python program to go onto the web, grab an HTML file and then parse the file specifically for the InChI string. Retrieving a valid result means that the user must input a chemical name that has a page designated on Wikipedia. Many chemicals have multiple names, so Wikipedia handles this through making the most commonly used name to be expressed in the URL (Uniform Resource Locator). All other naming formats will redirect to the URL that uses the chemicals common name.

Learning Objectives

  • Import Python Library
  • Create and Define Functions
  • Parse HTML Text
  • Display Results

Recommended Reading

  • Internet of Chemistry Things Activity 1 (https://ioct.tech/edu/ioct1) Page that explains basic Instructions for setting up Python on a computer. The Python Activities listed in the sidebar may also help to explain some of the background information.
  • Spring ChemInformatics OLCC Course (http://olcc.ccce.divched.org/Spring2017OLCC) This site provides lots of information on working with chemical data.
  • Python Documentation (https://docs.python.org/3/) Python 3 documentation that correlates to the version used within this tutorial.
  • Beautiful Soup Module  (https://www.crummy.com/software/BeautifulSoup/bs4/doc/) Documentation on the installation and use of this module with Python.

Methods

The Python File used in this tutorial can be located within the following GitHub Page along with a DOI (Digital Object Identifier) on FigShare.1 Python will run on many different operating systems, however this tutorial uses the Thonny IDE (Integrated Development Environment) to design, run and test the code.2 The following code will take a chemical name and insert this into a preformatted URL that will pull all of the html from a corresponding Wikipedia page. The code will then parse and separate out everything in the html from the InChI identifier displaying the results.

Python 3 has been used for all code in this tutorial so make sure to consult the correct version documentation if additional reference is needed. Should the syntax or format change with future updates to the Python Language, it may be necessary to approach the task in a different way. The steps are broken down into sections which should be placed into the file one after the other from top to bottom.

Step 1

Starting with the libraries and modules that need to be declared, enter the code in step 1. The first line will import a function called “urlopen” from a library module called “urllib.request”. This will be responsible for allowing the program to fetch URL’s. The second line will import a library called “BeautifulSoup” from the package “bs4”. This module will be responsible for isolating the html text that we would like to retrieve as a result. The last module that will be imported is called “re” and this will be used to make some regular expressions that look for the pattern defined in the programs code which will contain the results.

The Python documentation recommended may be helpful with getting a deeper understanding of how importation of libraries into the program works. Be sure that the following code in step 1 is placed at the top of the file.

from urllib.request import urlopen
from bs4 import BeautifulSoup
import re

Step 2

After making the imports, add the following code which will define the first variable stored by Python. The name of the variable will be called “chemical” and it will store the value given through the text input displayed to the user. The variable stored should be a type of chemical identified by either its common name or systematic chemical name.

chemical = input("Put in the name of the chemical you want the InChI for: ")

Step 3

Two more variables will be necessary in setting up the preformatted URL structure needed to find corresponding chemical page on Wikipedia. The following code will set “html” as the variable and it will be assigned a full URL that is a combination of a preformatted section that does not change along with a piece that takes the user input defined in step 2. The URL will be pieced together into a single string matching where the chemical page is located on Wikipedia. In programming, this process is often called concatenation. The command “urlopen” will serve as the function or assignment to that page defining how to use the variable when called. After the “html” variable has been stored, a second variable called “wikiExtract” will store the text retrieved from this webpage. The first piece in the parenthesis will define what variable to call for the URL assignment followed by the format and the command for what should be done. The command “get_text” will then store everything on the page to the variable defined.

html = urlopen("https://en.wikipedia.org/wiki/" + chemical)
wikiExtract = BeautifulSoup(html, "lxml").get_text()

Step 4

After the html of the webpage has been retrieved, the next few lines will search, isolate and store the InChI value independently of the other text from the webpage. The first line will perform a search of the html for the pattern “InChI=.*” and put this into memory as a value. The star and dot in the pattern will tell the program to grab everything found after “InChI=” as a parameter. Once the value has been found, the second line of code will then break off all text that follows the InChI string and store only the string to a new variable. The last function will provide the most refinement in isolating the InChI string by removing any added space or unwanted characters. The variable “inchiFinal” will be sent to the users display as the result of the search.

inchiMatch = re.findall("InChI=.*", wikiExtract)
inchiClean = inchiMatch[0].split('H\\')
inchiFinal = inchiClean[0].split()

Step 5

Before the user receives the results, the following code can be inserted to give a nice little message that is followed by the actual InChI string. This will help to keep things looking nice and clean.

print("\n" + "Wikipedia says the InChI is:" + '\n')
print(inchiFinal[0])

If you would like to just copy the entire program in sequence, below is the completed code containing everything that is needed to perform retrieving an InChI from Wikipedia.

Completed Code Example

from urllib.request import urlopen
from bs4 import BeautifulSoup
import re

chemical = input("Put in the name of the chemical you want the InChI for: ")

html = urlopen("https://en.wikipedia.org/wiki/" + chemical)
wikiExtract = BeautifulSoup(html, "lxml").get_text()

inchiMatch = re.findall("InChI=.*", wikiExtract)
inchiClean = inchiMatch[0].split('H\\')
inchiFinal = inchiClean[0].split()

print("\n" + "Wikipedia says the InChI is:" + '\n')
print(inchiFinal[0])

Program Demonstration

An interactive demo of this program is provided by Trinket in the online publication.5 The trinket can be visited at this location (https://trinket.io/python3/437c1f516a). The stored and printable copies will only contain screenshots below with descriptions as they cannot display the trinket program in a live environment.

Figure 1 The above image shows the interpreter requesting a chemical name from the user.

 

Figure 2 The above image shows the user inserting the chemical acetone into the request.

 

Figure 3 The above image shows the Python program displaying the resulting InChI after web scraping wikipedia for the answer.

 

References

(1) Cornell, A. Cheminformatics-Python. Figshare 2018. https://doi.org/10.6084/m9.figshare.7255901.
(2) Annamaa, A. Introducing Thonny, a Python IDE for Learning Programming. In Proceedings of the 15th Koli Calling Conference on Computing Education Research – Koli Calling ’15; ACM Press: Koli, Finland, 2015; pp 117–121. https://doi.org/10.1145/2828959.2828969.

Chemistry Programming with Python – Retrieving InChI From PubChem (Tutorial)

Andrew P. Cornell, Robert E. Belford

Chemistry Department, University of Arkansas at Little Rock, Little Rock, Arkansas 72204

 

Abstract

In this tutorial, a program written in Python will take a user specified chemical name and retrieve the associated chemical identifier or basic property using an online chemical database. This program can be used as both an aid for learning to programmatically work with chemical data and as a short general lesson for using Python with an API (Application Programming Interface). The database that will be used for this lesson is known as PubChem, which is a publicly accessible platform run by NCBI (National Center for Biotechnology Information).

PubChem offers public REST (Representational State Transfer) based programmatic access to a lot of the data contained on the servers which is defined with a specific syntax.1 Review the recommended reading to become familiar with the syntax if the need arises to pull data from PubChem that differs from this tutorial. This tutorial will use the InChI (International Chemical Identifier), InChI Key, molecular formula, Canonical SMILES (Simplified Molecular-Input Line-Entry System) and molecular weight with InChI being used in the demonstration section.2

Learning Objectives

  • Import Python Library
  • Create and Define Functions
  • Make API Request with Python
  • Parse and Display Results

Recommended Reading

  • Internet of Chemistry Things Activity 1 (https://ioct.tech/edu/ioct1) Page that explains basic Instructions for setting up Python on a computer. The Python Activities listed in the sidebar may also help to explain some of the background information.
  • Spring ChemInformatics OLCC Course (http://olcc.ccce.divched.org/Spring2017OLCC) This site provides lots of information on working with chemical data.
  • Python Documentation (https://docs.python.org/3/) Python 3 documentation that correlates to the version used within this tutorial.
  • PubChem REST Documentation (https://pubchemdocs.ncbi.nlm.nih.gov/pug-rest) Provides instructions on syntax structure, methods and request procedures for accessing data.

Methods

The files used in this tutorial can be located within the following GitHub Page (https://github.com/boots7458/Cheminformatics-Python) along with a DOI on FigShare (https://doi.org/10.6084/m9.figshare.7255901).3 Python will run on many different operating systems, however this tutorial will use the Thonny IDE (Integrated Development Environment) to design, run and test the code.4 The following code performs the commands that will retrieve either an InChI, InChI Key, molecular formula, SMILES or the molecular weight of a compound from PubChem. Each code section will be explained in detail with the full completed code located at the end of the tutorial. The completed code can be copied directly into a Python File and run as a fully functional program.

Python 3 has been used for all code in this tutorial so make sure to consult the correct version documentation if additional reference is needed. Should the syntax or format change with future updates to the Python Language, it may be necessary to approach the task in a different way. The steps are broken down into sections which should be placed into the file one after the other from top to bottom.

Step 1

When you run a python file it typically does not load user added modules.  These modules are stored in specific libraries and are only loaded when programs need those features.  So the first step will be to load a user requested library, the urllib.request – Extensible library for opening URLs (https://docs.python.org/3/library/urllib.request.html), which will define several functions and classes to help the program open URLs. This is done through the import command as shown in the following code.

import urllib.request

Step 2

After making the library import declaration, it is time to start defining a few functions that will build the different parts of the program. The first function is called “firstChoice” and will declare the “string2” variable as global. This will allow the variable to be called from any function without having to specifically pass it within the code. The function will be responsible for asking the user to input a text string that will be stored in memory as a variable for later use.

def firstChoice():
    global string2
    string2 = input("Enter a chemical name: ")

Step 3

The second function is much longer, despite performing a simple task. The function is called “choices” and it will ask the user to pick a number for which option to retrieve based on what the program is asking. The numbers inside the parenthesis next to the print statements define some formatting that will be displayed such as indents, line dividers and spacing for aesthetic purposes. The most important section is where “idChoice” is set as a global variable to store the value chosen by the user. The “if” and “elif” commands define what to do, depending on whether the user selects a valid number from the options or not. A valid option will simply pass the choice to be used in constructing the API (Application Programming Interface) URL for retrieving the user’s request. An incorrect option
will simply display a message stating the problem and to try again.

def choices():
    print(40 * "_")
    print(3 * " " + "Select the value below to retrieve")
    print(40 * "_")
    print('{:>23}'.format("INCHI[0]"))
    print('{:>23}'.format("INCHIKEY[1]"))
    print('{:>23}'.format("MOLECULAR FORMULA[2]"))
    print('{:>23}'.format("SMILES[3]"))
    print('{:>23}'.format("MOLECULAR WEIGHT[4]"))
    print(40 * "_")
    global idChoice
    idChoice = int(input("Enter a number choice? "))
    if 0 <= idChoice <= 4:
        choiceID()
    elif idChoice != range(0,4):
        print(2 * '\n' + 38 * '*')
        print("* Incorrect Number Choice, Try Again *")
        print(38 * '*' + 2 * '\n')
        choices()

Step 4

The next function in the program is called “choiceID” and this will take an array of values for the input and combine what the user has chosen into a string. The string will then be used for building the URL that will pull in the requested data value at the end. This is the function that will utilize the library from step 1 that was declared at the top of the file. The last part of the function will ask the user if another value should be requested or not. The program will end if the user types in “no” and will repeat the entire program if “yes” is entered as the user’s choice.

def choiceID():
    inputList = ['INCHI', 'INCHIKEY', 'MolecularFormula','CanonicalSMILES', 'MolecularWeight']
    selChoice = inputList[idChoice]

    string1 = "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/"
    string3 = "/property/" 
    string4 = "/TXT"
    html = urllib.request.urlopen(string1 + string2 + string3 + selChoice + string4).read()
    html2 = html.decode('UTF-8')
    print(2 * '\n')
    print(html2)
    print(2 * '\n')
    redoProg = input("Would you like to check another compound, yes or no? ")
    if redoProg == "yes":
        print ("\n" * 2)
        main()
    else:
        print("Done!")

Step 5

This function will be called “main” and it simply defines the order in which all other functions should be run. The program will run this first even though it is located towards the bottom of the Python File.

def main():
    firstChoice()
    choices()

Step 6

Here is where the program is told which function to start with and it says to start with the function titled “main” which will define the order of which individual functions to run. This line of code is the starting point at which the program will read from after all libraries have been loaded. It is very important that this line of code take place after the functions along with being loaded after “Import Time” so that libraries are loaded before any code is executed. For this reason “main()” is written last in the program.

## Program Assignments ##
main()

Completed Code Example

import urllib.request

def firstChoice():
    global string2
    string2 = input("Enter a chemical name: ")

def choices():
    print(40 * "_")
    print(3 * " " + "Select the value below to retrieve")
    print(40 * "_")
    print('{:>23}'.format("INCHI[0]"))
    print('{:>23}'.format("INCHIKEY[1]"))
    print('{:>23}'.format("MOLECULAR FORMULA[2]"))
    print('{:>23}'.format("SMILES[3]"))
    print('{:>23}'.format("MOLECULAR WEIGHT[4]"))
    print(40 * "_")
    global idChoice
    idChoice = int(input("Enter a number choice? "))
    if 0 <= idChoice <= 4:
        choiceID()
    elif idChoice != range(0,4):
        print(2 * '\n' + 38 * '*')
        print("* Incorrect Number Choice, Try Again *")
        print(38 * '*' + 2 * '\n')
        choices()
def choiceID():
    inputList = ['INCHI', 'INCHIKEY', 'MolecularFormula','CanonicalSMILES', 'MolecularWeight']
    selChoice = inputList[idChoice]

    string1 = "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/"
    string3 = "/property/" 
    string4 = "/TXT"
    html = urllib.request.urlopen(string1 + string2 + string3 + selChoice + string4).read()
    html2 = html.decode('UTF-8')
    print(2 * '\n')
    print(html2)
    print(2 * '\n')
    redoProg = input("Would you like to check another compound, yes or no? ")
    if redoProg == "yes":
        print ("\n" * 2)
        main()
    else:
        print("Done!")
def main():
    firstChoice()
    choices()
## Program Assignments ##
main()

NOTES ON USING THE PROGRAM
This program will take a single chemical name without any modification needed. For example, if the user wants to look for data related to acetone, then the term can be entered as just “acetone”. However, if a user wants to enter a chemical name that contains more than one word, then a place marker must be put in so that the spaces can be accounted for in the URL Syntax. To enter the term acetic acid, the user should enter “acetic%20acid” by putting %20 anywhere that should contain a space, or the program will throw several errors as a result.

Program Demonstration


An interactive demo of this program is provided by Trinket in the online publication.5 The trinket can be visited at this location (https://trinket.io/python3/c845b6bdbd). The stored and printable copies will only contain screenshots below with descriptions as they cannot display the trinket program in a live environment.

Figure 1 Shown above, “Acetone” has been inserted into the text input.

 

Figure 2 Shown above, option 1 for INCHIKEY has been selected.

 

Figure 3 Shown above is the InChI Key Result from the PubChem API.

Suggested Questions for Classroom Use:

  1. What is a library in python, and why would python use libraries
  2. What library was used in the above code, and what does it do?
  3. What command allows you to make a function in python?
  4. What are the names of the functions the above program created?
  5. What does the command “global” do? Why is it needed?
  6. What is the URL that is generated to get the molar mass of aspirin?
  7. What does the code ‘{:>23}’ do?
  8. For the following code
    idChoice = int(input("Enter a number choice? "))
        if 0 <= idChoice <= 4:
            choiceID()
        elif idChoice != range(0,4):
            print(2 * '\n' + 38 * '*')
            print("* Incorrect Number Choice, Try Again *")
            print(38 * '*' + 2 * '\n')
            choices()
    
      • (a) What does the following statements say?
    if 0 <= idChoice <= 4:
            choiceID()
    
      • (b) What does the following statements say? Can you think of another way of doing this?
    idChoice != range(0,4):
    
  9. Go to the Compound Property Tables on this page  https://pubchemdocs.ncbi.nlm.nih.gov/pug-rest$_Toc494865567
    • Add a 6th option (option 5) that would allow you to get the IUPAC Name for a compound
    • Add a 7th option (option 6) that would give you an indication if the compound is water or fat soluble.

References

(1) Kim, S.; Thiessen, P. A.; Cheng, T.; Yu, B.; Bolton, E. E. An Update on PUG-REST: RESTful Interface for Programmatic Access to PubChem. Nucleic Acids Research 2018, 46 (W1), W563–W570.
(2) Heller, S. R.; McNaught, A.; Pletnev, I.; Stein, S.; Tchekhovskoi, D. InChI, the IUPAC International Chemical Identifier. Journal of Cheminformatics 2015, 7 (1). https://doi.org/10.1186/s13321-015-0068-4.
(3) Cornell, A. Cheminformatics-Python. Figshare 2018. https://doi.org/10.6084/m9.figshare.7255901.
(4) Annamaa, A. Introducing Thonny, a Python IDE for Learning Programming. In Proceedings of the 15th Koli Calling Conference on Computing Education Research – Koli Calling ’15; ACM Press: Koli, Finland, 2015; pp 117–121. https://doi.org/10.1145/2828959.2828969.
(5) Elliott Hauser; Brian Marks; Ben Wheeler. Trinket; 2019.

PubChem chemical structure standardization

Abstract

Background:

PubChem is a chemical information repository, consisting of three primary databases: Substance, Compound, and BioAssay. When individual data contributors submit chemical substance descriptions to  substance, the unique chemical structures are extracted and stored into Compound through an automated process called structure standardization. The present study describes the PubChem standardization approaches and analyzes them for their success rates, reasons that cause structures to be  rejected, and modifcations applied to structures during the standardization process. Furthermore, the PubChem standardization is compared to the structure normalization of the IUPAC International Chemical Identifer (InChI) software, as manifested by conversion of the InChI back into a chemical structure.

Chemical Entity Semantic Specification: Knowledge representation for efficient semantic cheminformatics and facile data integration

Abstract

Background:

Over the past several centuries, chemistry has permeated virtually every facet of human lifestyle, enriching fields as diverse as medicine, agriculture, manufacturing, warfare, and electronics, among numerous others. Unfortunately, application-specific, incompatible chemical information formats and representation strategies have emerged as a result of such diverse adoption of chemistry. Although a number of efforts have been dedicated to unifying the computational representation of chemical information, disparities between the various chemical databases still persist and stand in the way of cross-domain, interdisciplinary investigations. Through a common syntax and formal semantics, Semantic Web technology offers the ability to accurately represent, integrate, reason about and query across diverse chemical information.

Results:

Here we specify and implement the Chemical Entity Semantic Specification (CHESS) for the representation of polyatomic chemical entities, their substructures, bonds, atoms, and reactions using Semantic Web technologies. CHESS provides means to capture aspects of their corresponding chemical descriptors, connectivity, functional composition, and geometric structure while specifying mechanisms for data provenance. We demonstrate that using our readily extensible specification, it is possible to efficiently integrate multiple disparate chemical data sources, while retaining appropriate correspondence of chemical descriptors, with very little additional effort. We demonstrate the impact of some of our representational decisions on the performance of chemically-aware knowledgebase searching and rudimentary reaction candidate selection. Finally, we provide access to the tools necessary to carry out chemical entity encoding in CHESS, along with a sample knowledgebase.

Conclusions:

By harnessing the power of Semantic Web technologies with CHESS, it is possible to provide a means of facile cross-domain chemical knowledge integration with full preservation of data correspondence and provenance. Our representation builds on existing cheminformatics technologies and, by the virtue of RDF specification, remains flexible and amenable to application- and domain-specific annotations without compromising chemical data integration. We conclude that the adoption of a consistent and semantically-enabled chemical specification is imperative for surviving the coming chemical data deluge and supporting systems science research.

Open Data, Open Source and Open Standards in chemistry: The Blue Obelisk five years on

Abstract

Background:

The Blue Obelisk movement was established in 2005 as a response to the lack of Open Data, Open Standards and Open Source (ODOSOS) in chemistry. It aims to make it easier to carry out chemistry research by promoting interoperability between chemistry software, encouraging cooperation between Open Source developers, and developing community resources and Open Standards.

Results:

This contribution looks back on the work carried out by the Blue Obelisk in the past 5 years and surveys progress and remaining challenges in the areas of Open Data, Open Standards, and Open Source in chemistry.

Conclusions:

We show that the Blue Obelisk has been very successful in bringing together researchers and developers with common interests in ODOSOS, leading to development of many useful resources freely available to the chemistry community.

UniChem: a unified chemical structure cross-referencing and identifier tracking system

Abstract

UniChem is a freely available compound identifier mapping service on the internet, designed to optimize the efficiency with which structure-based hyperlinks may be built and maintained between chemistry-based resources. In the past, the creation and maintenance of such links at EMBL-EBI, where several chemistry-based resources exist, has required independent efforts by each of the separate teams. These efforts were complicated by the different data models, release schedules, and differing business rules for compound normalization and identifier nomenclature that exist across the organization. UniChem, a large-scale, non-redundant database of Standard InChIs with pointers between these structures and chemical identifiers from all the separate chemistry resources, was developed as a means of efficiently sharing the maintenance overhead of creating these links. Thus, for each source represented in UniChem, all links to and from all other sources are automatically calculated and immediately available for all to use. Updated mappings are immediately available upon loading of new data releases from the sources. Web services in UniChem provide users with a single simple automatable mechanism for maintaining all links from their resource to all other sources represented in UniChem. In addition, functionality to track changes in identifier usage allows users to monitor which identifiers are current, and which are obsolete. Lastly, UniChem has been deliberately designed to allow additional resources to be included with minimal effort. Indeed, the recent inclusion of data sources external to EMBL-EBI has provided a simple means of providing users with an even wider selection of resources with which to link to, all at no extra cost, while at the same time providing a simple mechanism for external resources to link to all EMBL-EBI chemistry resources.

International chemical identifier for reactions (RInChI)

Abstract

The Reaction InChI (RInChI) extends the idea of the InChI, which provides a unique descriptor of molecular structures, towards reactions. Prototype versions of the RInChI have been available since 2011. The frst ofcial release (RInChIV1.00), funded by the InChI Trust, is now available for download (http://www.inchi-trust.org/downloads/). This release defnes the format and generates hashed representations (RInChIKeys) suitable for database and web operations. The RInChI provides a concise description of the key data in chemical processes, and facilitates the manipulation and analysis of reaction data.

Comparative evaluation of open source software for mapping between metabolite identifiers in metabolic network reconstructions: application to Recon 2

Abstract

Background:

An important step in the reconstruction of a metabolic network is annotation of metabolites. Metabolites are generally annotated with various database or structure based identifiers. Metabolite annotations in metabolic reconstructions may be incorrect or incomplete and thus need to be updated prior to their use.
Genome-scale metabolic reconstructions generally include hundreds of metabolites. Manually updating annotations is therefore highly laborious. This prompted us to look for open-source software applications that could facilitate automatic updating of annotations by mapping between available metabolite identifiers. We identified three applications developed for the metabolomics and chemical informatics communities as potential solutions. The applications were MetMask, the Chemical Translation System, and UniChem. The first implements a “metabolite masking” strategy for mapping between identifiers whereas the latter two implement different versions of an InChI based strategy. Here we evaluated the suitability of these applications for the task of mapping between metabolite identifiers in genome-scale metabolic reconstructions. We applied the best suited application to updating identifiers in Recon 2, the latest reconstruction of human metabolism.

Results:

All three applications enabled partially automatic updating of metabolite identifiers, but significant manual effort was still required to fully update identifiers. We were able to reduce this manual effort by searching for new identifiers using multiple types of information about metabolites. When multiple types of information were combined, the Chemical Translation System enabled us to update over 3,500 metabolite identifiers in Recon 2. All but approximately 200 identifiers were updated automatically.

Conclusions:

We found that an InChI based application such as the Chemical Translation System was better suited to the task of mapping between metabolite identifiers in genome-scale metabolic reconstructions. We identified several features, however, that could be added to such an application in order to tailor it to this task.

Consistency of systematic chemical identifiers within and between small-molecule databases

Abstract

Background:

Correctness of structures and associated metadata within public and commercial chemical databases
greatly impacts drug discovery research activities such as quantitative structure–property relationships modelling and compound novelty checking. MOL files, SMILES notations, IUPAC names, and InChI strings are ubiquitous file formats and systematic identifiers for chemical structures. While interchangeable for many cheminformatics purposes there have been no studies on the inconsistency of these structure identifiers due to various approaches for data integration, including the use of different software and different rules for structure standardisation. We have investigated the consistency of systematic identifiers of small molecules within and between some of the commonly used chemical resources, with and without structure standardisation.

Results:

The consistency between systematic chemical identifiers and their corresponding MOL representation varies greatly between data sources (37.2%-98.5%). We observed the lowest overall consistency for MOL-IUPAC names. Disregarding stereochemistry increases the consistency (84.8% to 99.9%). A wide variation in consistency also exists between MOL representations of compounds linked via cross-references (25.8% to 93.7%). Removing stereochemistry improved the consistency (47.6% to 95.6%).

Conclusions:

We have shown that considerable inconsistency exists in structural representation and systematic chemical identifiers within and between databases. This can have a great influence especially when merging data and if systematic identifiers are used as a key index for structure integration or cross-querying several databases. Regenerating systematic identifiers starting from their MOL representation and applying well-defined and documented chemistry standardisation rules to all compounds prior to creating them can dramatically increase internal consistency.

Towards a Universal SMILES representation – A standard method to generate canonical SMILES based on the InChI

Abstract

Background:

There are two line notations of chemical structures that have established themselves in the field: the SMILES string and the InChI string. The InChI aims to provide a unique, or canonical, identifier for chemical structures, while SMILES strings are widely used for storage and interchange of chemical structures, but no standard exists to generate a canonical SMILES string.

Results:

I describe how to use the InChI canonicalisation to derive a canonical SMILES string in a straightforward way, either incorporating the InChI normalisations (Inchified SMILES) or not (Universal SMILES). This is the first description of a method to generate canonical SMILES that takes stereochemistry into account. When tested on the 1.1 m compounds in the ChEMBL database, and a 1 m compound subset of the PubChem Substance database, no canonicalisation failures were found with Inchified SMILES. Using Universal SMILES, 99.79% of the ChEMBL database was canonicalised successfully and 99.77% of the PubChem subset.

Conclusions:

The InChI canonicalisation algorithm can successfully be used as the basis for a common standard for canonical SMILES. While challenges remain – such as the development of a standard aromatic model for SMILES – the ability to create the same SMILES using different toolkits will mean that for the first time it will be possible to easily compare the chemical models used by different toolkits.

Enhancement of the chemical semantic web through the use of InChI identifiers

Abstract

Molecules, as defined by connectivity specified via the International Chemical Identifier (InChI), are precisely indexed by major web search engines so that Internet tools can be transparently used for unique structure searches.

Detection of IUPAC and IUPAC-like chemical names

Abstract

Motivation:

Chemical compounds like small signal molecules or other biological active chemical substances are an important entity class in life science publications and patents. Several representations and nomenclatures for chemicals like SMILES, InChI, IUPAC or trivial names exist. Only SMILES and InChI names allow a direct structure search, but in biomedical texts trivial names and Iupac like names are used more frequent. While trivial names can be found with a dictionary-based approach and in such a way mapped to their corresponding structures, it is not possible to enumerate all IUPAC names. In this work, we present a new machine learning approach based on conditional random fields (CRF) to find mentions of IUPAC and IUPAC-like names in scientific text as well as its evaluation and the conversion rate with available name-to-structure tools.

Results:

We present an IUPAC name recognizer with an F1 measure of 85.6% on a MEDLINE corpus. The evaluation of different CRF orders and offset conjunction orders demonstrates the importance of these parameters. An evaluation of hand-selected patent sections containing large enumerations and terms with mixed nomenclature shows a good performance on these cases (F1 measure 81.5%). Remaining recognition problems are to detect correct borders of the typically long terms, especially when occurring in parentheses or enumerations. We demonstrate the scalability of our implementation by providing results from a full MEDLINE run.

Availability:

We plan to publish the corpora, annotation guideline as well as the conditional random field model as a UIMA component.

Contact:roman.klinger@scai.fraunhofer.de

QSPR modeling of octanol water partition coefficient of platinum complexes by InChI-based optimal descriptors

Abstract

Comparison of the quantitative structure—property relationships (QSPR) based on optimal descriptors calculated with the International Chemical Identifier (InChI) and QSPR based on optimal descriptors calculated with simplified molecular input line entry system has shown that the InChI-based optimal descriptors give more accurate prediction for the logarithm of octanol/water partition coefficient of platinum complexes.

Tautomer Identification and Tautomer Structure Generation Based on the InChI Code

Abstract

An algorithm is introduced that enables a fast generation of all possible prototropic tautomers resulting from the mobile H atoms and associated heteroatoms as defined in the InChI code. The InChI-derived set of possible tautomers comprises (1,3)-shifts for open-chain molecules and (1,n)-shifts (with n being an odd number >3) for ring systems. In addition, our algorithm includes also, as extension to the InChI scope, those larger (1,n)-shifts that can be constructed from joining separate but conjugated InChI sequences of tautomer-active heteroatoms. The developed algorithm is described in detail, with all major steps illustrated through explicit examples. Application to ∼72 500 organic compounds taken from EINECS (European Inventory of Existing Commercial Chemical Substances) shows that around 11% of the substances occur in different heteroatom−prototropic tautomeric forms. Additional QSAR (quantitative structure−activity relationship) predictions of their soil sorption coefficient and water solubility reveal variations across tautomers up to more than two and 4 orders of magnitude, respectively. For a small subset of nine compounds, analysis of quantum chemically predicted tautomer energies supports the view that among all tautomers of a given compound, those restricted to H atom exchanges between heteroatoms usually include the thermodynamically most stable structures.

yaInChI: Modified InChI string scheme for line notation of chemical structures

A modified InChI (International Chemical Identifier) string scheme, yaInChI (yet another InChI), is suggested as a method for including the structural information of a given molecule, making it straightforward and more easily readable. The yaInChI theme is applicable for checking the structural identity with higher sensitivity and generating three-dimensional (3-D) structures from the one-dimensional (1-D) string with less ambiguity than the general InChI method. The modifications to yaInChI provide non-rotatable single bonds, stereochemistry of organometallic compounds, allene and cumulene, and parity of atoms with a lone pair. Additionally, yaInChI better preserves the original information of the given input file (SDF) using the protonation information, hydrogen count +1, and original bond type, which are not considered or restrictively considered in InChI and SMILES. When yaInChI is used to perform a duplication check on a 3D chemical structure database, Ligand.Info, it shows more discriminating power than InChI. The structural information provided by yaInChI is in a compact format, making it a promising solution for handling large chemical structure databases.

Keywords: line notationduplication checkInChIchemical databaseSMILES

InChIKey collision resistance: an experimental testing

Abstract

InChIKey is a 27-character compacted (hashed) version of InChI which is intended for Internet and database searching/indexing and is based on an SHA-256 hash of the InChI character string. The first block of InChIKey encodes molecular skeleton while the second block represents various kinds of isomerism (stereo, tautomeric, etc.). InChIKey is designed to be a nearly unique substitute for the parent InChI. However, a single InChIKey may occasionally map to two or more InChI strings (collision). The appearance of collision itself does not compromise the signature as collision-free hashing is impossible; the only viable approach is to set and keep a reasonable level of collision resistance which is sufficient for typical applications. We tested, in computational experiments, how well the real-life InChIKey collision resistance corresponds to the theoretical estimates expected by design. For this purpose, we analyzed the statistical characteristics of InChIKey for datasets of variable size in comparison to the theoretical statistical frequencies. For the relatively short second block, an exhaustive direct testing was performed. We computed and compared to theory the numbers of collisions for the stereoisomers of Spongistatin I (using the whole set of 67,108,864 isomers and its subsets). For the longer first block, we generated, using custom-made software, InChIKeys for more than 3 × 1010 chemical structures. The statistical behavior of this block was tested by comparison of experimental and theoretical frequencies for the various four-letter sequences which may appear in the first block body. From the results of our computational experiments we conclude that the observed characteristics of InChIKey collision resistance are in good agreement with theoretical expectations.

InChI: connecting and navigating chemistry

Abstract

The International Chemical Identifier (InChI) has had a dramatic impact on providing a means by which to
deduplicate, validate and link together chemical compounds and related information across databases. Its influence
has been especially valuable as the internet has exploded in terms of the amount of chemistry related information
available online. This thematic issue aggregates a number of contributions demonstrating the value of InChI as an
enabling technology in the world of cheminformatics and its continuing value for linking chemistry data.

InChI in the wild: an assessment of InChIKey searching in Google

Abstract

While chemical databases can be queried using the InChI string and InChIKey (IK) the latter was designed for open-web searching. It is becoming increasingly effective for this since more sources enhance crawling of their websites by the Googlebot and consequent IK indexing. Searchers who use Google as an adjunct to database access may be less familiar with the advantages of using the IK as explored in this review. As an example, the IK for atorvastatin retrieves ~200 low-redundancy links from a Google search in 0.3 of a second. These include most major databases and a very low false-positive rate. Results encompass less familiar but potentially useful sources and can be extended to isomer capture by using just the skeleton layer of the IK. Google Advanced Search can be used to filter large result sets. Image searching with the IK is also effective and complementary to open-web queries. Results can be particularly useful for less-common structures as exemplified by a major metabolite of atorvastatin giving only three hits. Testing also demonstrated document-to-document and document-to-database joins via structure matching. The necessary generation of an IK from chemical names can be accomplished using open tools and resources for patents, papers, abstracts or other text sources. Active global sharing of local IK-linked information can be accomplished via surfacing in open laboratory notebooks, blogs, Twitter, figshare and other routes. While information-rich chemistry (e.g. approved drugs) can exhibit swamping and redundancy effects, the much smaller IK result sets for link-poor structures become a transformative first-pass option. The IK indexing has therefore turned Google into a de-facto open global chemical information hub by merging links to most significant sources, including over 50 million PubChem and ChemSpider records. The simplicity, specificity and speed of matching make it a useful option for biologists or others less familiar with chemical searching. However, compared to rigorously maintained major databases, users need to be circumspect about the consistency of Google results and provenance of retrieved links. In addition, community engagement may be necessary to ameliorate possible future degradation of utility.

UniChem: extension of InChI-based compound mapping to salt, connectivity and stereochemistry layers

Abstract

UniChem is a low-maintenance, fast and freely available compound identifier mapping service, recently made available on the Internet. Until now, the criterion of molecular equivalence within UniChem has been on the basis of complete identity between Standard InChIs. However, a limitation of this approach is that stereoisomers, isotopes and salts of otherwise identical molecules are not considered as related. Here, we describe how we have exploited the layered structural representation of the Standard InChI to create new functionality within UniChem that integrates these related
molecular forms. The service, called ‘Connectivity Search’ allows molecules to be first matched on the basis of complete identity between the connectivity layer of their corresponding Standard InChIs, and the remaining layers then compared to highlight stereochemical and isotopic differences. Parsing of Standard InChI sub-layers permits mixtures and salts to also be included in this integration process. Implementation of these enhancements required simple modifications to the schema, loader and web application, but none of which have changed the original UniChem functionality or services. The scope of queries may be varied using a variety of easily configurable options, and the output is annotated to assist the user to filter, sort and understand the difference between query and retrieved structures. A RESTful web service output may be easily processed programmatically to allow developers to present the data in whatever form they believe their users will require, or to define their own level of molecular equivalence for their resource, albeit within the constraint of identical connectivity.

On InChI and Evaluating the Quality of Cross-reference Links

Abstract

Background: There are many databases of small molecules focused on different aspects of research and its applications. Some tasks may require integration of information from various databases. However, determining which entries from different databases represent the same compound is not straightforward. Integration can be based, for example, on automatically generated cross-reference links between entries. Another approach is to use the manually curated links stored directly in databases. This study employs well-established InChI identifiers to measure the consistency and completeness of the manually curated links by comparing them with the automatically generated ones.

Results: We used two different tools to generate InChI identifiers and observed some ambiguities in their outputs. In part, these ambiguities were caused by indistinctness in interpretation of the structural data used. InChI identifiers were used successfully to find duplicate entries in databases. We found that the InChI inconsistencies in the manually curated links are very high (28.85% in the worst case). Even using a weaker definition of consistency, the measured values were very high in general. The completeness of the manually curated links was also very poor (only 93.8% in the best case) compared with that of the automatically generated links.

Conclusions: We observed several problems with the InChI tools and the files used as their inputs. There are large gaps in the consistency and completeness of manually curated links if they are measured using InChI identifiers. However, inconsistency can be caused both by errors in manually curated links and the inherent limitations of the InChI method.

IUPAC STANDARDS ONLINE

Abstract

IUPAC Standards Online is a database built from IUPAC’s (The International Union of Pure and Applied Chemistry) standards and recommendations, which are extracted from the journal Pure and Applied Chemistry (PAC).

The International Union of Pure and Applied Chemistry (IUPAC) is the organization responsible for setting the standards in chemistry that are internationally binding for scientists in industry and academia, patent lawyers, toxicologists, environmental scientists, legislation, etc. “Standards” are definitions of terms, standard values, procedures, rules for naming compounds and materials, names and properties of elements in the periodic table, and many more.

The database will be the only product that provides for the quick and easy search and retrieval of IUPAC’s standards and recommendations which until now have remained unsorted within the huge Pure and Applied Chemistry archive.

Covered topics:

Analytical Chemistry
Biochemistry
Chemical Safety
Data Management
Education
Environmental Chemistry
Inorganic Chemistry
Materials
Medicinal Chemistry
Nomenclature and Terminology
Nuclear Chemistry
Organic Chemistry
Physical Chemistry
Theoretical & Computational Chemistry
Toxicology

Current Status and Future Development in Relation to IUPAC Activities

Abstract

The IUPAC International Chemical Identifier (InChI) is a non-proprietary, machine-readable chemical structure representation format enabling electronic searching, and interlinking and combining, of chemical information from different sources. It was developed from 2001 onwards at the U.S. National Institute of Standards and Technology under the auspices of IUPAC’s Chemical Identifier project. Since 2009, the InChI Trust, a consortium of (mostly) publishers and software developers, has taken over responsibility for funding and oversight of InChI maintenance and development. Funding and responsibility for scientific aspects of InChI development remain with the IUPAC Division VIII (Chemical Nomenclature and Structure Representation) and InChI Subcommittee.

Applications of the InChI in cheminformatics with the CDK and Bioclipse

Abstract

Background

The InChI algorithms are written in C++ and not available as Java library. Integration into software written in Java therefore requires a bridge between C and Java libraries, provided by the Java Native Interface (JNI) technology.

Results

We here describe how the InChI library is used in the Bioclipse workbench and the Chemistry Development Kit (CDK) cheminformatics library. To make this possible, a JNI bridge to the InChI library was developed, JNI-InChI, allowing Java software to access the InChI algorithms. By using this bridge, the CDK project packages the InChI binaries in a module and offers easy access from Java using the CDK API. The Bioclipse project packages and offers InChI as a dynamic OSGi bundle that can easily be used by any OSGi-compliant software, in addition to the regular Java Archive and Maven bundles. Bioclipse itself uses the InChI as a key component and calculates it on the fly when visualizing and editing chemical structures. We demonstrate the utility of InChI with various applications in CDK and Bioclipse, such as decision support for chemical liability assessment, tautomer generation, and for knowledge aggregation using a linked data approach.

Conclusions

These results show that the InChI library can be used in a variety of Java library dependency solutions, making the functionality easily accessible by Java software, such as in the CDK. The applications show various ways the InChI has been used in Bioclipse, to enrich its functionality.

Keywords:

InChI, InChIKey, Chemical structures, JNI-InChI, The Chemistry Development Kit, OSGi, Bioclipse, Decision
support, Linked data, Tautomers, Databases, Semantic web

Application of InChI to curate, index, and query 3-D structures

Abstract

The HIV structural database (HIVSDB) is a comprehensive collection of the structures of HIV protease, both of unliganded enzyme and of its inhibitor complexes. It contains abstracts and crystallographic data such as inhibitor and protein coordinates for 248 data sets, of which only 141 are from the Protein Data Bank (PDB). Efficient annotation, indexing, and querying of the inhibitor data is crucial for their effective use for technological and industrial applications. The application of IUPAC International Chemical Identifier (InChI) to index, curate, and query inhibitor structures HIVSDB is described. Proteins 2005. Published 2005 Wiley‐Liss, Inc.

Additive InChI-based optimal descriptors: QSPR modeling of fullerene C60 solubility in organic solvents

Abstract

Optimal descriptors calculated with International Chemical Identifier (InChI) have been used to construct one-variable model of the solubility of fullerene C60 in organic solvents . Attempts to calculate the model for three splits into training and test sets gave stable results.

IUPAC InChI (Video)

This presentation is a part of Google Tech Talks which was added to the GoogleTalksArchive on August 22, 2006. The original presentation date took place on November 2, 2006.

ABSTRACT (Imported From YouTube Source)

The central token of information in Chemistry is a chemical substance, an entity that can often be represented as a well-defined chemical structure. With InChI we have a means of representing this entity as a unique string of characters, which is otherwise represented by various of 2-D and 3-D chemical drawings, ‘connection tables’ and synonyms. InChI therefore represents a discrete physical entity, to which is associated as array of chemical properties and data. NIST has long been involved in disseminating chemical reference data associated with such discrete substances. A InChI is therefore the key index to this data. Many other types of data and information are also naturally tied to it, including biological information, commercial availability, toxicity, drug effectiveness and so forth. Because of the diversity of properties and interactions of a chemical substance, effective location of chemical information generally requires further qualifiers, which may be represented coarsely as a key word, but more precisely using a controlled vocabulary. There are no simple separations between information sought by difference disciplines and for different objectives. However, reference data may be organized according the disciplines most directly involved in making the measurements: -isolated substance – mass, infrared, NMR, spectra; physical properties -substance in the context of others – solubility, affinity, .. -properties of a mixture containing the substance The desired data can be a number, vector or image, usually associated with dimensions and links to source information. In some cases, this information is typically converted to a curve or diagram for use by an expert and may be further processed by specialized software. In other cases, a single numerical values is the target. Also, some complexities of structure that must be dealt with in practical search is represented in InChI, but must be decoded for use in searching.

isoenum – a python package to enumerate isotopically resolved InChI

Isotopic (iso) enumerator (enum) – enumerates isotopically resolved InChI (International Chemical Identifier) for metabolites.

The isoenum Python package provides command-line interface that allows you to enumerate the possible isotopically-resolved InChI from one of the Chemical Table file (CTfile) formats (i.e. molfile, SDfile) used to describe chemical molecules and reactions as well as from InChI itself.

https://github.com/MoseleyBioinformaticsLab/isoenum