InChI Tag: English

78 posts

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

RDKit InChI Calculation with Jupyter Notebook

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

 

Batch Chemical IDs Conversion in Spreadsheets

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 (video)

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.

InChI Student Worksheet

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.

QSAR-modeling of toxicity of organometallic compounds by means of the balance of correlations for InChI-based optimal descriptors

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 diversity14(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.”

 

InChI-based optimal descriptors: QSAR analysis of fullerene[C60]-based HIV-1 PR inhibitors by correlation balance

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, q2LOO = 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, Rm2 = 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.

Use of the international chemical identifier for constructing QSPR-model of normal boiling points of acyclic carbonyl substances

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).

The Chemical Translation Service—a web-based tool to improve standardization of metabolomic report

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

Failures of fractional crystallization: ordered co‐crystals of isomers and near isomers

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 AB interactions are clearly better than the average of the homomolecular AA and BB 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.

Simplified molecular input-line entry system and International Chemical Identifier in the QSAR analysis of styrylquinoline derivatives as HIV-1 integrase inhibitors

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.

Representation of chemical structures

Abstract:
At the root of applications for substructure and similarity searching, reaction retrieval, synthesis planning, drug discovery, and physicochemical property prediction is the need for a machine‐readable representation of a structure. Systematic nomenclature is unsuitable, and notations and fragment codes have been superseded, except in certain specific applications. Connection tables are widely used, but there is no formal standard. Recently the International Union of Pure and Applied Chemistry (IUPAC) International Chemical Identifier (InChI) has started to attract interest. This review also summarizes the representation of chemical reactions and three‐dimensional structures.

InChI: a user’s perspective

Exchange of chemical structures between practicing chemists is essential to chemical communication. The International Chemical Identifier (InChI) provides a means for lossless communication of structures without resort to any proprietary software or databases nor does it require any payment or royalty fees. This perspective describes why the InChI is valuable to all chemists and how it will be an essential component of creating the chemical web.

InChI As a Research Data Management Tool

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]

 

On InChI and evaluating the quality of cross-reference links

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.

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

Chambers et al. Journal of Cheminformatics 2014, 6:43

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.

Keywords: UniChem, Standard InChI, InChIKey, Chemical databases, Data integration, Connectivity search

Data Formats for Elementary Gas Phase Kinetics, Part 1: Unique Representations of Species at the Molecular Level

BURGESS, D. R., MANION, J. A. and HAYES, C. J. (2014), Data Formats for Elementary Gas Phase Kinetics, Part 1: Unique Representations of Species at the Molecular Level. Int. J. Chem. Kinet., 46: 640-650. doi:10.1002/kin.20875

Abstract

Standardized electronic formats for data are needed to efficiently and transparently communicate the results of scientific studies. A format for the unique identification of chemical species is a requirement in the field of chemistry, and the IUPAC International Chemical Identifier (InChI) has been widely adopted for this purpose. The InChI identifier has proved to be very useful. The InChI identifier, however, is currently insufficient to uniquely specify some types of molecular entities at a detailed molecular level needed to fully characterize their chemical nature, to differentiate between chemically distinct conformers, to uniquely identify structures used in quantum chemical calculations, and to completely describe elementary chemical reactions. To address this limitation, we propose an augmented form of InChI, denoted as InChI–ER, which contains additional optional layers that allow the unique and unambiguous identification of molecules at a detailed molecular level. The new layers proposed herein are optional extensions of the existing InChI formalism and, like all other InChI layers, would not interfere with InChI identifiers currently in use. The focus of the present work is the better specification of required molecular entities such as rotational conformations, ring conformations, and electronic states. In companion articles, we propose additional reaction layers using an extended InChI format that will enable the unique identification of elementary chemical reactions, including specification of associated transition states, specification of the changes in bonds that occur during reaction, and classification of reaction types.

Applications of the InChI in cheminformatics with the CDK and Bioclipse

Spjuth et al. Journal of Cheminformatics 2013, 5:14

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

CVDHD: a cardiovascular disease herbal database for drug discovery and network pharmacology

Gu et al. Journal of Cheminformatics 2013, 5:51

Abstract
Background: Cardiovascular disease (CVD) is the leading cause of death and associates with multiple risk factors.
Herb medicines have been used to treat CVD long ago in china and several natural products or derivatives (e.g.,
aspirin and reserpine) are most common drugs all over the world. The objective of this work was to construct a
systematic database for drug discovery based on natural products separated from CVD-related medicinal herbs and
to research on action mechanism of herb medicines.

Description: The cardiovascular disease herbal database (CVDHD) was designed to be a comprehensive resource for
virtual screening and drug discovery from natural products isolated from medicinal herbs for cardiovascular-related
diseases. CVDHD comprises 35230 distinct molecules and their identification information (chemical name, CAS registry
number, molecular formula, molecular weight, international chemical identifier (InChI) and SMILES), calculated molecular
properties (AlogP, number of hydrogen bond acceptor and donors, etc.), docking results between all molecules and
2395 target proteins, cardiovascular-related diseases, pathways and clinical biomarkers. All 3D structures were optimized
in the MMFF94 force field and can be freely accessed.

Conclusions: CVDHD integrated medicinal herbs, natural products, CVD-related target proteins, docking results, diseases
and clinical biomarkers. By using the methods of virtual screening and network pharmacology, CVDHD will provide a
platform to streamline drug/lead discovery from natural products and explore the action mechanism of medicinal herbs.
CVDHD is freely available at http://pkuxxj.pku.edu.cn/CVDHD.

Keywords: Cardiovascular disease, Drug discovery, Network pharmacology, Molecular docking, Virtual screening, Herbal
formula, Natural products, Medicinal herbs, Traditional Chinese medicine

Matlab InChIKey Scripts

This is a collection of Matlab scripts for working with InChIKeys: IKextract, IKfreqFH, IKstring, and IKmusic

IKextract, InChIKey Extract, can extract InChIKeys from chemical Structure data files (SDFs). This script was successfully used to extract over 90 million InChIKeys (unique chemical identifiers) from over 5000 PubChem SD files. Users can also extract other data from SDFs by specifying the desired SD tag.

IKfreqFH, InChIKey frequency of first hash block, extracts the first hash block of InChIKeys and sorts them by frequency. Such a method is useful for analyzing the variety of chemical connectivity in large datasets.

IKstring, InChIKey String, allows for searching for strings within InChIKeys. I use it to search the > 90 million InChIKeys in PubChem.

IKmusic, InChIKey music, creates music from InChIKeys. A unique song is created for each InChIKey (i.e. every unique chemical substance has a different song!)

Identifier conversion on an Excel spreadsheet

This resource is a simple spreadsheet in Excel that provides a handy interconversion between different chemical identifiers, namely name, InChI, InChIKey and SMILES. It uses some web services to do translations, i.e. PubChem PUG REST and NCI/CADD Chemical Identifier Resolver.

This workbook does not uses macros but makes use of the WEBSERVICE function added to Excel for Windows in Excel 2013.

 

Many InChIs and quite some feat

Comprehensive 2015 article published in Springer’s Journal of Computer-Aided Molecular Design. Here is the abstract,

The IUPAC International Chemical Identifier (InChI) is a non-proprietary, international standard to represent chemical structures. It was conceived 15 years ago, and has been is use for 10 years. The InChI Trust is developing and improving on the current standard, further enabling the interlinking of chemical structures on the web. This mini-review looks at the widespread adoption of InChI in software and databases.