The current methods of publishing chemical information in bioscience articles are analysed. Using 3 papers as use-cases, it is shown that conventional methods using human procedures, including cut-and-paste are time-consuming and introduce errors. The meaning of chemical terms and the identity of compounds is often ambiguous. valuable experimental data such as spectra and computational results are almost always omitted. We describe an Open XML architecture at proof-of-concept which addresses these concerns. Compounds are identified through explicit connection tables or links to persistent Open resources such as PubChem. It is argued that if publishers adopt these tools and protocols, then the quality and quantity of chemical information available to bioscientists will increase and the authors, publishers and readers will find the process cost-effective.
In an accompanying paper1 we have argued the value of extracting the chemical information in bioscientific research, transforming it to XML and redisseminating it openly. The present article expands on the technical and cultural infrastructure required to support this. The technical aspects have been solved to proof-of-concept stage and we are starting to embark on experiments in the social domain. In this we thank BMC for inviting us to submit this and we present a model here which we believe could be attractive for bioscience publishers and their community.
We concentrate on the current publication of chemistry in bioscience. This includes:
This type of chemistry is very well understood and has a simple ontology which has not changed over decades2. Unlike much bioscience, where ontological tools are an essential part of reconciling the domain-dependent approaches, much chemistry has an implicitly agreed abstract description. The problems are primarily reconciling syntax and semantics. This is because chemists use abbreviated and lazy methods of communicating data, relying on trained readers to add information from the context. We have reviewed current problems of machine-understanding of chemistry3 in a typical chemistry journal, many of which are perpetuated by the graphical orientation of conventional publishing houses. Here we take the view that a committed publishing house can create a cost-effective and human-tolerable system for authoring semantically correct chemistry in (bio)scientific documents.
We know from experience that Utopian visions do not sell themselves. The enormous and accepted value of the sequence and structures databases arose not from the demands of individual authors, but from wider communities of researchers, funders, and learned societies. Even now the deposition of protein structure data, without which journals will not generally accept a paper, is seen as at best a chore and at worst as the donation of information to competitors. Without that commitment and the resource, however, Structural Biology would not exist as a discipline. Here we present the following vision; that aggregated "small-molecule" chemical information, if deposited at publication, aggregated and disseminated, would be seen as worth paying the prices of inconvenience.
For this proposal we make some assumptions about the evolving informatics environment:
We look to bioscience to take a lead in helping realise the following vision. On the positive we now believe that there are already enough Open tools and Open resources which with communal will among bioscience authors and publishers can make the vision attractive and cost-effective.
Much chemical data is largely context-free in that it can be understood and recreated independently of the location or motivation. The primary data model is over 120 years old and was developed by Beilstein in the 19th century and comprises three components: the chemical compound, its properties and citations. A pure compound is described by an immutable structural formula and has precisely reproducible properties. There are qualifications (e.g. some properties may depend on the precise crystalline form) but it has served as the basis of a multimillion chemical information market, with the compound at the centre. Current thinking asserts that the biological action of a compound is, in principle, reproducible and predictable if the system is carefully enough replicated and the components understood. This is the central dogma of the chemically-based pharmaceutical industry.
Chemistry has a tradition of quality through properties and analysis, so every new compound (and many resynthesised ones) mentioned in the literature must be accompanied by measurements of properties to justify identity and purity. These facts are available, in text form, in the primary literature in which over a million new compounds are published annually. Because structure predicts properties, and because drug discovery is so difficult, the pharmaceutical industry tests many compounds for biological activity. It is therefore the primary financial engine for the chemical information industry.
Techniques for managing items 1-5 listed above such as aggregating chemical compounds, properties and for searching the results, are very well understood and can be easily made nearly automatic. Most of the information of benefit to the community exists on the authors' computers in machine-processable form. It can be automatically converted into fine-grained XML4 with almost no loss. The chemist has electronic copies of molecular structures, spectra and properties whose semantics are extremely well understood and where a simple technical protocol for conversion to XML and hence publication can be created. To support this part of the data publication process we have created the XML-based Chemical Markup Language (CML).5 The primary information components (all of which are common and well understood) are:
The identification of chemical entities is the most valuable contribution that an author can make. In most cases s/he (as, say, the purchaser or creator of the materials) is the best judge of what was used . It is more considerably more difficult to identify compounds after publication as we show below.
We list possible methods of publishing the identity of compounds in machine-understandable form:
Chemical names can be used with more or less specificity. Thus "1,4-dichlorobenzene" is unambiguous in any context. However there are several areas where more generic language is used. This can arise because:
"Glutamic acid" is an example of ambiguity through unspecified stereochemistry. Thus PubChem lists the three isomers (Table 1)
A structure without any stereo information is more valuable than one with partial information of unknown quality. The InChI9 is an extremely powerful tool here. We have recently shown that "staurosporine" reported in publications and suppliers catalogs contains many instances of incorrect stereochemistry, and some partially correct and incorrect. Given that this is a single substance, its structure and absolute configuration has been known for a considerable time there is no reason for using any structure other than PubChem CID: 44259
The preceeding discussion shows how ambiguity and loss of information can occur if structured procedures are not followed. The following examples (Table 2) show some suggested approaches to markup which can re-capture much of the information loss described above.
The last example references a generic name, monosaccharide, in the IUPAC guide2 to organic nomenclature with a suggested use of identifiers.
In this second section, we take 3 articles from BMC publications and show the success and problems of extracting chemistry in machine-understandable form. These have been randomly selected and do not necessarily reflect the average quality of BMC publications. We note that in our other studies of chemical text very few publications were error-free.
The abstract is typical of the discourse:
Background: Recent studies indicate that the G protein-coupled receptor (GPCR) signaling machinery can serve as a direct target of reactive oxygen species, including nitric oxide (NO) and S-nitrosothiols (RSNOs). To gain a broader view into the way that receptor-dependent G protein activation - an early step in signal transduction - might be affected by RSNOs, we have studied several receptors coupling to the Gi family of G proteins in their native cellular environment using the powerful functional approach of [35S]GTP?S autoradiography with brain cryostat sections in combination with classical G protein activation assays.
Results: We demonstrate that RSNOs, like S-nitrosoglutathione (GSNO) and S-nitrosocysteine (CysNO), can modulate GPCR signaling via reversible, thiol-sensitive mechanisms probably involving S-nitrosylation. RSNOs are capable of very targeted regulation, as they potentiate the signaling of some receptors (exemplified by the M2/M4 muscarinic cholinergic receptors), inhibit others (P2Y12 purinergic, LPA1lysophosphatidic acid, and cannabinoid CB1 receptors), but may only marginally affect signaling of others, such as adenosine A1, µ-opioid, and opiate related receptors. Amplification of M2/M4 muscarinic responses is explained by an accelerated rate of guanine nucleotide exchange, as well as an increased number of high-affinity [35S]GTP?S binding sites available for the agonist-activated receptor. GSNO amplified human M4 receptor signaling also under heterologous expression in CHO cells, but the effect diminished with increasing constitutive receptor activity. RSNOs markedly inhibited P2Y12 receptor signaling in native tissues (rat brain and human platelets), but failed to affect human P2Y12 receptor signaling under heterologous expression in CHO cells, indicating that the native cellular signaling partners, rather than the P2Y12 receptor protein, act as a molecular target for this action.
Conclusion: These in vitro studies show for the first time in a broader general context that RSNOs are capable of modulating GPCR signaling in a reversible and highly receptor-specific manner. Given that the enzymatic machinery responsible for endogenous NO production is located in close proximity with the GPCR signaling complex, especially with that for several receptors whose signaling is shown here to be modulated by exogenous RSNOs, our data suggest that GPCR signaling in vivo is likely to be subject to substantial, and highly receptor-specific modulation by NO-derived RSNOs.
The above contains reference to a considerable numbers of individual compounds. The authors helpfully publish a table of abbreviations to assist in the compound identification process (Figure 2).
Using this as our data, we have attempted to identify (Table 3) the "small-molecules" mentioned in the discourse. Using PubChem and occasional suppliers catalogs, the elapsed real time was about 1 hour. It can be seen that of 19 molecules, 15 were identified without problems or error, 2 were not (CysNOGly and Glu-CysNO) and 2 required additional expertise by the reader. We estimate that it would take an author the same amount of time to add PubChem IDs for novel compounds and much less time if they were in common use in their laboratory.
It is only a little additional effort to convert each molecule to a more formal description expressed in e.g. CML5 and which can carry not only an atom connection table and the corresponding InChI identifer, but also molecule "meta-data" describing the provenance of the information:
<cml:molecule xmlns:cml="http://www.xml-cml.org/schema/cml2/core" title="carbacholine">
<cml:metadataList title="generated automatically from Openbabel">
<cml:metadata name="dc:creator" content="OpenBabel version 1-100.1"/>
<cml:metadata name="dc:description" content="Conversion of legacy filetype to CML"/>
<cml:metadata name="dc:identifier" content="InChI"/>
<cml:metadata name="dc:content"/>
<cml:metadata name="dc:rights" content="open"/>
<cml:metadata name="dc:type" content="chemistry"/>
<cml:metadata name="dc:contributor" content="rzepa"/>
<cml:metadata name="dc:creator" content="Openbabel V1-100.1"/>
<cml:metadata name="dc:date" content="Tue May 17 12:02:50 BST 2005"/>
<cml:metadata name="cmlm:structure" content="yes"/>
</cml:metadataList>
<cml:identifier convention="iupac:inchi">InChI=1/C6H14N2O2.ClH/c1-8(2,3)4-5-10-6(7)9;/h4-5H2,1-3H3,(H-,7,9);1H</cml:identifier>
<cml:atomArray atomID="a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13"
elementType="N C C O C O N C C C H H Cl"
formalCharge="1 0 0 0 0 0 0 0 0 0 0 0 -1"
x2="-1.892900 -1.178500 -0.464000 0.250500 0.964900 0.964900 1.761800 -2.305400 -2.476300 -1.480400 2.174300 2.476300 -1.921800"
y2="0.415300 0.827800 0.415300 0.827800 0.415300 -0.409700 0.628800 1.129800 -0.168000 -0.299200 1.343300 0.216300 -1.343300"/>
<cml:bondArray atomRef1="a1 a1 a1 a1 a2 a3 a4 a5 a5 a7 a7"
atomRef2="a2 a8 a9 a10 a3 a4 a5 a6 a7 a11 a12"
order="1 1 1 1 1 1 1 2 1 1 1"/>
</cml:molecule>
Such molecular datuments can be embedded in any XML-based document in a manner which can if needed survive e.g. journal production processes, and where the molecular information can be extracted and re-used at any stage.
Our critique of the chemistry requires context, given by the abstract:
Abstract Background: Kynureninase is a key enzyme on the kynurenine pathway of tryptophan metabolism. One of the end products of the pathway is the neurotoxin quinolinic acid which appears to be responsible for neuronal cell death in a number of important neurological diseases. This makes kynureninase a possible therapeutic target for diseases such as Huntington's, Alzheimer's and AIDS related dementia, and the development of potent inhibitors an important research aim.
Results: Two new kynurenine analogues, 3-hydroxydesaminokynurenine and 3- methoxydesaminokynurenine, were synthesised as inhibitors of kynureninase and tested on the tryptophan-induced bacterial enzyme from Pseudomonas fluorescens, the recombinant human enzyme and the rat hepatic enzyme. They were found to be mixed inhibitors of all three enzymes displaying both competitive and non competitive inhibition. The 3-hydroxy derivative gave low Ki values of 5, 40 and 100 nM respectively. [...]
Conclusion: For kynureninase from all three species the 2-amino group was found to be crucial for activity whilst the 3-hydroxyl group played a fundamental role in binding at the active site presumably via hydrogen bonding. The potency of the various inhibitors was found to be species specific. The 3-hydroxylated inhibitor had a greater affinity for the human enzyme, consistent with its specificity for 3-hydroxykynurenine as substrate, whilst the methoxylated version yielded no significant difference between bacterial and human kynureninase. [...]
We note that "quinolinic acid" has 4 mentions in the text, but its formula is not given. We took 2.7 minutes to identify CID1066 in PubChem, with the additional useful information (from Medline/MeSH):
A metabolite of tryptophan with a possible role in neurodegenerative disorders. Elevated CSF levels of quinolinic acid are correlated with the severity of neuropsychological deficits in patients who have AIDS
The name "3-hydroxydesaminokynurenine" [the synthesized compound (4)] presents a more serious problem. Although the structure is given in a diagram, the stereogenic centre is not marked. It would be a reasonable assumption that "kynurenine" refers to a natural product which is only found in one enantiomeric form and "desamino" was also chiral. Careful reading (requiring chemical expertise) showed that the authors had probably synthesised a racemic mixture, since they started with achiral compounds and did not report chiral reagents or a resolution step. The compound should have been reported as (R/S)-3-hydroxydesaminokynurenine or (much better) as the IUPAC-like name "IUPAC Name: (R/S) 2-amino-4-(3-hydroxy-phenyl)-4-oxo-butanoic acid". Indeed many referees and editors would have insisted on this specification. In the event, as we show below, this is not the reported compound!
The tools we are proposing would immediately have queried both these concerns at time of authoring and, had they been available to the technical editor would have produced a more useful and more easily readable paper.
The publication of the synthesis or re-synthesis of compounds must be accompanied by analytical and property data to prove purity and identitity. The ritualistic presentation shown below (Figure 3) as copied from the manuscript is entirely typical of most chemical publications. Note that it is visually challenging to read and this is entirely due to the publisher's requirements of using a system designed to save paper rather than communicate useful information.
For each compound this compressed information is (manually) created from some or all of:
For the publication, the authors have to measure peak heights from the spectrum (possibly with a ruler), and transcribe them to a Word or PDF format, probably by typing the values or cut-n-pasting them. We have developed an Open Source robot (OSCAR)8 which can understand this data if it is syntactically correct, and the result is shown in Figure 4:
The coloured parts are those that adhere to the publication guidelines. We found 7 changes that had to be made to the punctuation (missing punctuation, syntactic variation is common in many chemical papers). OSCAR can then understand and check the data. For compound [4] it announces
There are fewer H atoms by NMR integration (7) than there are by elemental analysis (12)
This is acceptable because there are exchangeable groups. However it also announces:
There are more C-NMR environments (11) than there are C atoms from elemental analysis (10).
as it found the string "114.47 120.78". We also do not understand this and it may be an error (or it could be a solvent peak or other impurity). OSCAR also had problems interpreting the chemical formula: "C11H14NO4" which in fact turns out to be a charged species. In fact the compounds are poorly identified. They appear to be not the aminoacids "3-Hydroxydesaminokynurenine (4)" and "3-Methoxydesaminokynurenine (5)" but their hydrochloride salts. This is not a trivial error; the melting points and infrared spectra of the parents and their salts will be significantly different and would cause errors if transcribed unthinkingly from the paper.
Even with OSCAR it took one of us ca 45 minutes to make sure that the above analysis was correct. From several anecdotal conversations with typical authors we estimate that it took about 2 hours to prepare this part of the submission; a thorough reviewer might take 0.5 hour to decipher it. All of this is unnecessary if the original connection tables, spectra and analytical data were made available in uncorrupted form. As it is, much of the original data is lost; using the reported peaks OSCAR does its best to recreate what the spectrum might have looked like (Figure 5). Precise peak shapes and traces of impurities are lost in this representation.
Here too a number of small-molecules are reported without formulae;
Background [...] Phenols and anilines are generally recognized as substrates of the heme peroxidases (donor: H2O2 oxidoreductases EC 1.11.17). The peroxidases catalyze oxidation of the substrates by hydrogen peroxide or alkyl peroxides, usually but not always, via free-radical intermediates [1,2]. Nonphenolic compounds, such as indole-3-acetic acid, phenylenediamines, ferrocenes, phenothiazines, phenoxazines, have also been investigated as peroxidase substrates [2][3-5]. Steady-state kinetics of peroxidase action has been described as a ping-pong scheme with compound I and compound II formation [1].
This paper also has issues with the identity of compounds.
This is again a visually unacceptable format dictated by the prevailing business model of chemical publishing. Note "Napthyl" is misspelt, presumably because it has been (mis)typed by the authors, which would give unnecessary problems to chemical text-mining robots. Worse, the identity of AHA5 is genuinely unclear, in that the connection could be to either of the phenyl groups in the fragment: "Ph-C(O)N(OH)-Ph". BHA (also described elsewhere by "benzhydroxamic acid") has no structural or compositional formula. Worse, BHA in the PDB ligand collection refers to 2-hydroxy-4-amino-benzoic acid (a completely different compound); "benzhydroxamic acid" has code BHO.
Another section of this article describes various computational modelling techniques applied to these molecules; here we can assume that the authors had precise coordinates for all the computed species available at the end of the research, although none of this data is actually made available via the final published article. Some of this data is used to drive a docking program, which itself implies a protocol used to specify various run-time parameters. Some of these are declared in the article, many probably default to values set internally within the program. There are also ambiguities in the declared computational protocol:
The optimized geometry of molecules was used for energies and charges calculations with a 6-31G basis set using RHF and B3PW91 (Density Functional Theory).
Here, the RHF and the B3PW91 protocols are mutually exclusive; either one or the other could have been used, but not in combination. Mapping either protocol to e.g. the appropriate input for the program package used can also be a challenge for anyone not totally familiar with the program; program manuals are still designed largely for human rather than machine use. Such ambiguities, and lack of data, make repetition of the modelling more difficult for others.
It should now be clear that the current system of communicating chemistry (which is common to all publishers and all disciplines) is inefficient, costly, lossy, and of questionable quality. We present a new XML-based approach which we show:
We note that when starting to draft a publication the author already has
Electronic lab notebook technology is not well advanced in chemistry; our architecture would provide a good method for preserving conventional data. It looks as shown in Figure 7 (blue = XML):
The author would then use a tool which can manage structured XML documents and provide normal textual support (spellchecks, etc.). There are 4 additional tools required to support chemical information:
... When foobarone is broken down, the presence of indole can be detected ...
might be marked up as
... When <cml:molecule name="foobarone" dictRef="natprod:foobarone"/> is broken down, the presence of <cml:molecule> <identifier convention="iupac:inchi" title="indole">1/C8H7N/c1-2-4-8-7(3-1)5-6-9-8/h1-6H,9H</identifier> </cml:molecule> indole can be detected ...
yield(93%), M.Pt. 273-275oCbecomes
<cml:list>
<cml:property dictRef="cml:yield">
<cml:scalar units="cml:percent">93%lt;/cml:scalar>
</cml:property>
<cml:property dictRef="cml:mpt">
<cml:scalar units="cml:celsius" minValue="273" maxvalue="275"/>
</cml:property>
</cml:list>
The result is a single structured XML "datument"16 containing fine-grained markup of facts (molecules, measurements, properties, chemical names). This datument can be used to create derivatives such as the "full-text" or the "supplemental data". The complete datument (if Open) or the "data" if not is then reposited (XX) where it can be harvested. New compounds with their names are fed back into the lexicon and all compound/property data is available for datamining and computational re-use (e.g. for further in silico prediction. A human or robot reader has access to the same lexicons and dictionaries as the author so that the semantics and ontology of authoring are the same as those of reading (and of preservation).
The social aspects of metadata and rights were addressed in (1). To meet these we place special emphasis on the XML and its metadata. Fine-grained XML (e.g. <scalar>...</scalar> or <molecule>...</molecule> represents facts which can be identified as Open and not the property of the publisher. Hyperlinks and structure for semantics (e.g. identification of compounds in PubChem) are also Open. Tools such as XSLT can then extract the factual, non-copyrightable information with little technical problem. Rights should be explicitly marked up. If the publisher supports Open Access and also Open Data then it is valuable to label the appropriate components with Open licenses, such as the RDF metadata provided by Creative Commons. It is also possible to preserve authors' moral rights and provenance of data re-used within the paper (e.g. spectra of molecules or coordinates of protein structures).
The transition to this architecture will have a cost, so short term-benefits are particularly attractive. Moreover most of the parties are not used to a communal approach (pressures are normally per-institution and per-publisher).
The benefits should also be clear for most individuals and organisations:
Because the chemical information is structured we now have a biocheminformatics cycle (this term - with spelling as here - is in modest use. We suggest its adoption to describe the management of chemical information in biosciences and not just in biochemistry) where, for the first time, large scale robotic data analysis can take place (Figure 8).
The data in the research (laboratory, in silico, or both) are published in a lossless manner. Molecules and their properties have unique identifiers as described above and can be integrated into mainstream bioinformatics in the same manner as collections such as PubChem, MSDChem (at EBI), KEGG, etc. They will bring the added value of consistently captured property data and spectra. We also expect that many in silico properties will then be systematically added.
The introduction of structured authoring tools will help this process considerably. Templates can be created for the chemical components described above and where the information exists in XML (connection tables, spectra, properties) it should be as easy as for committed authors as using a semantically void tool (e.g. Word). Where information needs to be converted from legacy formats we have created Open Web Services which publishers (and authors) may clone and customise. The main technical challenge will be the management of chemical names in free text.
The analysis presented here introduces the basic concepts of chemistry in bioinformatics. Many areas remain to be addressed; we briefly describe two below which have immediate application.
Chemical reactions are very patchily abstracted from the literature and the products are almost always closed. The motivation for the primary publication of reactions in bioscience includes:
CMLReact (an extension of CML) has been created19 to support these catagories of reaction. Items 1-2 require identical support as in mainstream chemistry (e.g. in journals supporting organic synthesis). Item 3 can be supported by CMLReact though there is little current experience. Item 4 is supported by SBML20 and efforts such as BioPAX21 (in which CML is a tool). Item 5 is particularly exciting and exemplified by our MACiE database22 where 150+ enzymes with 3D structures and proposed mechanisms have been collected. Currently the abstraction is manual and expensive, but if the ideas in the current paper are implemented we shall present an extension whereby mechanisms can be relatively cheaply captured at source. This would be a major new resource in bioinformatics.
The primary motivation for a publication, of course, is citability and the technology we describe raises the fear among chemists that the data in it might actually be read, analysed and re-used. However it also raises the vision of changing the "citation economy" (which values market perception) to a "reuse economy" where a the data in an article (or as we prefer, a "datument") are valued by how often they are re-used.
| Table 1. Isomers of Glutamic acid | |
|---|---|
| CID | name(s) |
| 611 | glutamic acid |
| 33032 | L-glutamic acid |
| 23327 | D-glutamic acid |
| Table 2. Examples of approaches to chemical Identification. | ||
|---|---|---|
| Prose description | More precise suggested naming using the CML5 approach | Type of information |
| Acetaldehyde has a general narcotic action | <p><cml:molecule> <cml:identifier convention="iupac:inchi">1/C2H4O/c1-2-3/h2H,1H3</cml:identifier> <cml:identifier convention="pubchem:CID">177</cml:identifier> </cml:molecule> has a ...</p> | precise, redundant |
| Benzo(a)pyrene is a potent mutagen and carcinogen | <p><cml:molecule><cml:identifier convention="pubchem:CID">2336</cml:identifier></cml:molecule> is a ...</p> | precise |
| glycine (1mmol) was added ... | <p><cml:molecule title="glycine"><cml:identifier convention="iupac:inchi">1/C2H5NO2/c3-1-2(4)5/h1,3H2,(H,4,5)</cml:identifier></cml:molecule> is a ...</p> | hydrogens mobile |
| calculations on glycine zwitterion... | <p><cml:molecule title="g><cml:identifier convention="pubchem:CID">InChI=1/C2H5NO2/c3-1-2(4)5/h1H3,3H2</cml:identifier></cml:molecule> is a ...</p> | hydrogens precise |
| ... a monosaccharide transporter... | <p>a <cml:molecule title="monosaccharide"><cml:identifier convention="iupac:carbohydrate">2-Carb-2</cml:identifier></cml:molecule> transporter ...</p> | |
| Table 3. Identification of Small-molecules noted in Ref. 14 | |||
|---|---|---|---|
| abbrev | author name | PubChem ID | Notes |
| 2MeSADP | 2-methylthio-ADP | [121990] | Not found directly in PubChem. Located in supplier's catalog. Synonym from that found in PubChem |
| 5-HT | 5-hydroxytryptamine | 5202 | |
| CCh | carbacholine | 521353 | |
| CP-55940 | (-)-3-[2-hydroxy-4-(1,1-dimethylheptyl)-phenyl]-4-[3-hydroxypropyl]cyclohexan-1-ol | 104895 | IUPAC: 5-(1,1-dimethylheptyl)-2-[5-hydroxy-2-(3-hydroxypropyl)cyclohexyl]-phenol |
| CysNO | S-nitrosocysteine | 39933 | |
| CysNOGly | S-nitroso-cysteinyl-glycine | Text search on PubChem found wrong compound. Not found in major supplier | |
| DAMGO | [D-Ala2, N-Me-Phe4, Gly5-ol]-enkephalin | 104742 | |
| DPCPX | 8-cyclopentyl-1,3-dipropylxanthine | 1320 | |
| DTT | dithiotreitol | 19001 | |
| Glu-CysNO | L-?-glutamyl-S-nitrosocysteine | Identity unresolved | |
| GSH | glutathione | 745 | |
| GSNO | S-nitrosoglutathione | 104858 | |
| LPA | lysophosphatidic acid | 3987 | |
| NA | noradrenaline | 951 | PubChem CID covers both racemic and d-enantiomer |
| NO | nitric oxide | 84878 | PubChem also lists 945 (with incorrect formula HNO) as nitric oxide |
| NOBF4 | nitrosodium tetrafluoroborate | 151929 | Paper has a typographical error for "nitrosonium". Structure in PubChem is wrong (formula should be NO+BF4-, not H2NO+.BF4-) |
| SNAP | S-nitroso-N-acetyl-D,L-penicillamine | 5231 | PubChem does not list stereochemistry |
| RSNO | S-nitrosothiol | Appears to be a generic compound (R-S-N=O) | |
| SNP | sodium nitroprusside | 26256 | |