InChI Posts

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.

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.

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.

Abstract

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

Abstract

Learn about technology that solves the issue of interpreting 3D stereochemical information implied in 2D structure representations.

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.

Project for Cheminformatics Fall 2012. Part 2/2. Presentation on encodings, SMILES and InChI.

Abstract

Abstract

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.

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.

Capturing mixture composition: an open machine-readable format for representing mixed substances

Alex M. Clark, Leah R. McEwen, Peter Gedeck & Barry A. Bunin
Journal of Cheminformatics volume 11, Article number: 33 (2019)

Abstract: We describe a file format that is designed to represent mixtures of compounds in a way that is fully machine readable. This Mixfile format is intended to fill the same role for substances that are composed of multiple components as the venerable Molfile does for specifying individual structures. This much needed datastructure is intended to replace current practices for communicating information about mixtures, which usually relies on human-readable text descriptions, drawing several species within a single molecular diagram, or mutually incompatible ad hoc solutions. We describe an open source software application for editing mixture files, which can also be used as web-ready tools for manipulating the file format. We also present a corpus of mixture examples, which we have extracted from collections of text-based descriptions. Furthermore, we present an early look at the proposed IUPAC Mixtures InChI specification, instances of which can be automatically generated using the Mixfile format as a precursor.

An Update on the Open Source InChI Project

Steve Heller and Igor Plasnof ?

This post consist of a simple spreadsheet that takes that splits an InChI in its layers to facilitate its conceptualisation and its teaching. It considers the six layers currently detailed in the InChI TechnicalFAQ, https://www.inchi-trust.org/wp/technical-faq-2/#4.3.

The spreadsheet also facilitates looking up an InChI by entering the molecule name or its SMILES representation.

This RDKit InChI Calculation with Jupyter Notebook tutorial is useful to teach the basics of how to interact with InChI using a cheminformatics toolkit in a Jupyter Notebook. The notebook has the following learning objectives:

1. Setup RDKit with a Jupyter Notebook
2. Construct a molecule (RDKit molecular object) from a SMILES string
3. Display molecule images
4. Calculate an InChI for a molecule
5. Calculate InChIs for a list of molecules

InChI OER poster presented at the American Chemical Society Central Regional Meeting Jun  2019 in Midland, MI.

Common tools for conversions, including some spreadsheet-based options included in this site, are hard to use for hundred or thousands of compounds we may want to use in cheminformatics projects. This resource includes a diferent approach to the conversion. By using the PubChem Power User Gateway it allows converting hundreds of chemical identifiers on a single call the a webservice.

Two files are included in this OER: an Excel file, that includes two UDF functions for doing the conversions, documentation and examples; and a VBA module that can be imported to any Excel file to include this functions to any existing spreadsheet.

2012 San Diego ACS presentation: Registration system of mcule: InChI is the key

Mcule provides virtual screening services on the web to help identifying novel drug candidates by screening different databases. For these databases, it is essential to have a robust molecule registration system not depending on different drawing conventions, tautomeric states, etc. It is critical to assure that the same compounds get the same IDs and, most importantly, different compounds never get the same ID. To the best of our knowledge, InChI provides the best solution for this problem. In this presentation we would like to summarize how InChI is implemented into the mcule registration system and how it is used effectively with our vendor database and open registration services.

This document contains a brief intro to InChI suitable for undergraduate students and two exercises, with answer keys. The first assignment asks about the information encoded in a sample InChI. The last question in this assignment asks students to use the InChI Key as a search term – this will be a lot easier to do if this information is available digitally so that students can simply copy and paste the InChI key rather than typing it by hand into wikipedia.

The second exercise asks students to draw several simple organic compounds with an appropriate computer application and generate the InChI and InChI key. Most commonly used structure drawing programs will readily generate the InChI and InChI key for a structure. In addition there are a number of online services that have a structure drawing application that will generate an InChI or InChI key. Grading this exercise will be much easier if done digitally.

Both exercises were written with the intention that students would complete them on line using a Learning Management System (LMS) such as Blackboard, Canvas, Moodle, etc., where the students would copy and paste an appropriate InChI or InChI KEY into a text box, which the LMS would compare with the correct answer which was submitted by the instructor.

Toropov, A. A., Toropova, A. P., & Benfenati, E. (2010). QSAR-modeling of toxicity of organometallic compounds by means of the balance of correlations for InChI-based optimal descriptors. Molecular diversity, 14(1), 183-192.

This paper present a use of InChI-based molecular descriptors to predict toxicity. Its abstract follows.

“Quantitative structure–activity relationships (QSAR) for toxicity toward rats (pLD50) have been built by means of optimal descriptors. Comparison of the optimal descriptors calculated using the International Chemical Identifier (InChI) with the optimal descriptors calculated using the simplified molecular input line entry system (SMILES) has shown that the InChI-based models give more accurate prediction for the abovementioned toxicity of organometallic compounds. These models were obtained by means of the balance of correlation: one subset of the training set (subtraining set) plays role of the training; the second subset (calibration set) plays role of the preliminary check of the models. It has been shown that the balance of correlations is a more robust predictor for the toxicity than the classic scheme (training set—test set: without the calibration set). Three splits into the subtraining set, calibration set, and test set were examined.”

The International Chemical Identifier (InChI) has been used to construct InChI-based optimal descriptors to model the  binding affinity for fullerene[C60]-based inhibitors of human immunodeficiency virus type 1 aspartic protease (HIV-1 PR). Statistical characteristics of the one-variable model obtained by the balance of correlations are as follows: n = 8, r² = 0.9769, q²_LOO = 0.9646, s = 0.099, F = 254 (subtraining set); n = 7, r² = 0.7616, s = 0.681, F = 16 (calibration set); n = 5, r² = 0.9724, s = 0.271, F = 106, R_m² = 0.9495 (test set). Predictability of this approach has been checked with three random splits of the data: into the subtraining set, calibration set, and test set.

Optimal descriptors calculated with international chemical identifier have been used to construct one-variable model of the normal boiling points of acyclic carbonyl substances. Attempts to calculate the model for three splits into training and test sets gave stable results. Statistical quality of the model is n = 150, r ² = 0.9825, s = 4.96 °C, F = 8,312 (training set) and n = 50, r ² = 0.9791, s = 4.68 °C, F = 2,249 (test set).

Summary: Metabolomic publications and databases use different database identifiers or even trivial names which disable queries across databases or between studies. The best way to annotate metabolites is by chemical structures, encoded by the International Chemical Identifier code (InChI) or InChIKey. We have implemented a web-based Chemical Translation Service that performs batch conversions of the most common compound identifiers, including CAS, CHEBI, compound formulas, Human Metabolome Database HMDB, InChI, InChIKey, IUPAC name, KEGG, LipidMaps, PubChem CID+SID, SMILES and chemical synonym names. Batch conversion downloads of 1410 CIDs are performed in 2.5 min. Structures are automatically displayed.

Implementation: The software was implemented in Groovy and JAVA, the web frontend was implemented in GRAILS and the database used was PostgreSQL.

Availability: The source code and an online web interface are freely available. Chemical Translation Service (CTS): http://cts.fiehnlab.ucdavis.edu

A list of 270 structures of ordered co‐crystals of isomers, near isomers and molecules that are almost the same has been compiled. Searches for structures containing isomers could be automated by the use of IUPAC International Chemical Identifier (InChI™) strings but searches for co‐crystals of very similar molecules were more labor intensive. Compounds in which the heteromolecular A…B interactions are clearly better than the average of the homomolecular A…A and B…B interactions were excluded. The two largest structural classes found include co‐crystals of configurational diastereomers and of quasienantiomers (or quasiracemates). These two groups overlap. There are 114 co‐crystals of diastereomers and the same number of quasiracemates, with 71 structures being counted in both groups; together the groups account for 157 structures or 58% of the total. The large number of quasiracemates is strong evidence for inversion symmetry being very favorable for crystal packing. Co‐crystallization of two diastereomers is especially likely if a 1,1 switch of a methyl group and an H atom, or of an inversion of a [2.2.1] or [2.2.2] cage, in one of the diastereomers would make the two molecules enantiomers.

The simplified molecular input-line entry system (SMILES) and IUPAC International Chemical Identifier (InChI) were examined as representations of the molecular structure for quantitative structure-activity relationships (QSAR), which can be used to predict the inhibitory activity of styrylquinoline derivatives against the human immunodeficiency virus type 1 (HIV-1). Optimal SMILES-based descriptors give a best model with n = 26, r(2) = 0.6330, q(2) = 0.5812, s = 0.502, F = 41 for the training set and n = 10, r(2) = 0.7493, r(pred)(2) = 0.6235, R(m)(2) = 0.537, s = 0.541, F = 24 for the validation set. Optimal InChI-based descriptors give a best model with n = 26, r(2) = 0.8673, q(2) = 0.8456, s = 0.302, F = 157 for the training set and n = 10, r(2) = 0.8562, r(pred)(2) = 0.7715, R(m)(2) = 0.819, s = 0.329, F = 48 for the validation set. Thus, the InChI-based model is preferable. The described SMILES-based and InChI-based approaches have been checked with five random splits into the training and test sets.

Chemistry International, Volume 38, Issue 3-4, Pages 24–26

Abstract

Progress in science has always been driven by data as a primary research output. This is especially true of the data-centric fields of molecular sciences. Scholarly journals in chemistry in the 19th century captured a (probably small) proportion of research data in printed journals, books, and compendia. The curation of this data from its origins in the 1880s and for most of the 20th century was largely driven by a few organisations as a commercial and proprietary activity. The online era, dating from around 1995, saw much experimentation centred around the presentation and delivery of journals, but less so of the data. The latter evolved, almost by accident, into what is now known as electronic supporting or supplemental information (SI), associated with journal articles. [1] That there was still a general problem in science was revealed by the “Climategate” events in 2009, where a lack of access to the data on which climate models are based induced all manner of unfortunate conspiracy theories. [2] These events catalysed a change in policy at, amongst others, UK research funders. One outcome of this change was seen in May 2015 with the introduction of new research data management (RDM) requirements for funded researchers. This centred around the precept that primary research data should be made openly available [3] and coincided with the evolution of the open science tripod of open data, open access articles, and open science notebooks. [4]

A video describing the origin of the InChI.

Using InChI to link different data sources.

Using the InChI Key as a search term.

Video introduction to InChI.

Galgonek and Vondrášek Journal of Cheminformatics 2014, 6:15

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.