Henry Rzepa's blog

Calicheamicin was noted in the previous post as a natural product with antitumour properties and having many weird structural features such as  an unusual “enedidyne” motif. The representation is shown below.

A partial structure shown below for Calicheamicin replaces the -(CH₂)4- substructure with a four carbon chain that includes two sp²centres instead of two sp³ centres. The purpose is to find out how these structural modifications to the classic Bergman affect the mechanism.

TS1 is shown below for this model and the computed free energy barrier for this cyclisation is 42.5 kcal/mol at the uωB97XD/Def2-TZVPP level, <S²> = 0.345. FAIR Data DOI: 10.14469/hpc/14583[1]. This compares with 33.0 kcal/mol calculated for the -(CH₂)4- version, for which <S²> = 0.266. To prepare for modelling the full Calicheamicin molecule, the basis set for this model was reduced to Def2-SVPP and at this level ΔG^‡ was 43.0 kcal/mol, <S²> = 0.368, the difference being small enough that the reduction in basis set seems unlikely to affect the results. The C-C bond forming lengths are 1.957 (Deft-TZVPP) and 1.989Å (Def2-SVPP).

Now for a larger model containing the entire Calicheamicin molecule. Two possibilities were explored; one where the geometry of the system was fully optimised in isolation to yield a conformation for Calicheamicin which folded in upon itself and for which ΔG^‡ (Def2-SVPP) 40.1 kcal/mol, <S²> 0.368.

The second model used the initial geometry of Calicheamicin as obtained from a crystal structure of the ligand folded into the minor grove of a DNA fragment and which has a much more linear form. The reactant in this mode was +6.1 kcal/mol higher in energy than the previous and TS1 was 4.6 kcal/mol higher, leading to ΔG^‡ 38.6 kcal/mol, <S²> 0.367.

So what conclusions can we draw from these extended models of the Bergman cyclisation? The activation free energies for all three models are in the range 42.5 – 38.6 kcal/mol, which is a great deal higher than a value commensurate with a facile room temperature reaction (~22±3). The observation that Calicheamicin can in fact be characterised as a crystal structure when bound to DNA suggests that the cyclisation barrier cannot be too low, but conversely the range 42.5 – 38.6 kcal/mol appears too large for Calicheamicin to easily activate into a biradical in order to abstract hydrogen atom and end up causing strand scission. Might the simplistic model of a split UHF wavefunction resulting in values of <S²> 0.37 be the problem? Well, a similar approach was taken to modelling the Stevens rearrangement [2]. Using a plain non-biradical closed shell wavefunction, a barrier of ~48 kcal/mol was obtained, but this reduced to 14 kcal/mol when the UHF method was applied (<S²>  0.421), so this model appears to work well in those circumstances. The jury must still be out on whether the Bergman cyclisation mechanism is being correctly modelled here or whether something more complex is going on.

References

1. H. Rzepa, "Mechanism of the Masamune-Bergman reaction. Calicheamicin", 2024. https://doi.org/10.14469/hpc/14583
2. H. Rzepa, "The Stevens rearrangement: how history gives us new insights.", 2021. https://doi.org/10.59350/4010f-fvr26

Calicheamicin is a natural product with antitumour properties discovered in the 1980s, with the structure shown below. As noted elsewhere, this structure has many weird properties, including amongst other features an unusual “enedidyne” motif and the presence of an iodo group on an aromatic ring. Its isolated 3D structure is quite difficult to get hold of (embedded structures in a DNA fragment are available however); the 3D model associated with the Wikipedia entry is essentially only in 2D. The representation shown below, including the absolute stereochemistry, was obtained from the SciFinder entry.

As a prelude to modelling the mechanism of the Bergman cyclisation (for Part 1 in which a simple cycloendiyne is explored, see DOI: 10.59350/jczra-f0r90 [1]) of the enediyne ring on this actual molecule, a 3D model was constructed.^‡ One possible such model is shown below, built to maximise wherever possible interactions such as hydrogen bonds and weak dispersion attractions from eg methyl groups. A side benefit of doing this is the natural emergence of a “cavity” in which the very large iodine atoms snuggles, as it happens adjacent to the enediyne component – something you would not naturally infer from the structure representation shown above! A spacefill model of this conformation is shown below (click on the image to get an interactive version), emerging from an ωB97XD/Def2-SVPP energy minimisation (DOI: 10.14469/hpc/14586).[2]

The below shows a crystal structure (2pik) of Calicheamicin embedded into a DNA duplex,^† which shows a stretched linear conformation of Calicheamicin rather than the compact form more appropriate for an isolated molecule.

The next step was to use the ωB97XD/Def2-SVPP wavefunction[2] to calculate the full electron density for the molecule, and using this to evaluate the NCI (non-covalent-interaction) isosurfaces. These are shown below, and the eye is immediately drawn to the regions surrounding that iodine atom, which are replete with attractive green surfaces. Blue and cyan coloured surfaces derive from hydrogen bonds formed within the 3D structure (click on the image to get an interactive version, but be patient, it takes a little while to load).

The next stage, using the model to evaluate the energetics of the Masamune-Bergman cyclisation for Calicheamicin itself will be reported in part 3.

^‡For those interested, this was constructed in stages. The structure representation had been drawn in Chemdraw, saved as a pseudo 3D molfile and then loaded into Gaussview. There, it was subjected to several cycles of energy minimisation using the MMFF94 molecular mechanics force field. The stereochemistry of all the centres was carefully checked at each stage, if necessary corrected and re-optimised. The next stage was to subject it to a PM7 semiempirical SCF minimisation, a method which includes dispersion attraction terms and which tends to give geometries that are quite close to eg those obtained using dispersion-corrected DFT methods, in this example ωB97XD/Def2-SVPP.

References

1. H. Rzepa, "Mechanism of the Masamune-Bergman reaction. Part 1.", 2024. https://doi.org/10.59350/jczra-f0r90
2. H. Rzepa, "Calicheamicin, full system, reactant, wB97XD/Def2-svpp G = -5768.785232", 2024. https://doi.org/10.14469/hpc/14586

The Masamune-Bergman reaction[1],[2] is an example of  a highly unusual class of chemical mechanism[3] involving the presumed formation of the biradical species shown as Int1 below by cyclisation of a cycloenediyne reactant. Such a species is  so reactive that it will be quickly trapped, as for example by dihydrobenzene to form the final product. This cycloenediyne is not just an obscure chemical curiosity, the motif is incorporated into the natural product Calicheamicin, which is a potent antitumor antibiotic discovered in the 1980s. This drug owes its activity to the cyclisation TS1 shown below, which for n=2 occurs at the low temperature of 310K. The resulting biradical Int1 is a potent hydrogen abstractor, the species acting this way for hydrogen atoms associated with deoxyribose of DNA, ultimately leading to strand scission. Although I have explored many a mechanism on this blog using computational methods, I have never included any biradical examples. Here I explore the computational aspects of this reaction, and also include a pathway proceeding vis TS2- Int2 – TS3 in which hydrogen abstraction precedes cyclisation, in order to see how competitive such an alternative might be as a function of the ring size (n in scheme below).

The computational procedure was ωB97XD/Def2-TZVPP and the FAIR data is collected at DOI: 10.14469/hpc/14546 [4]. A spin unrestricted procedure is adopted using an approximation to allow for biradicaloid species, namely an initial first guess at the wavefunction using the keyword guess(mixed) which mixes what would be the HOMO and the LUMO of the molecule in a closed shell sense to allow a combination which includes an open shell singlet with one electron in the HOMO and one electron in the LUMO (a biradical). Part of the purpose of this approach is to try to find out if it gives reasonable results for such a mechanism. I will introduce the spin expectation operator <S²> to help identify biradicals. For closed shell singlets it has the value  0.0, for a pure biradical it has the value 1.0. Thus for species  Int1, the values are typically ~0.995 and for the preceding TS1 ~ 0.3 to 0.57. IRC (Intrinsic reaction coordinate) calculations for TS1 show a smooth transition from values of <S²> = 0.0 (Reactant) through to 1.0 (Int1).

The results are shown below for three values of n, revealing that as the ring size increases (ending with an acyclic system Et₂) the free energy barrier increases significantly, as indeed is reported[1],[2]. The alternative pathway proceeding via TS2 is always higher in free energy and varies much less with ring size. This route can therefore be firmly excluded from contention.

†<S²> =0.27. ^‡Final Product (n=2)  = -620.327868 (-116.1 kcal/mol)

This computational modelling largely agrees with the observations made for this reaction, with just one inconsistency. For n=2, the reaction is reported as taking place at 37°C, for which a typical free energy barrier would be in the region of ~24±2 kcal/mol,[5] around 9 kcal/mol lower than the computed value at this level of theory. This could originate from either a deficiency in the computational model, possibly in the handling of the open shell biradicaloid character by use of a simple spin unrestricted model,[6] or incursion of some lower energy process into the mechanism (free radical involvement?). I will continue probing this issue to see if its origins can be identified.

In the next part of this blog, I will investigate the mechanism as applied to Calicheamicin to see how the more complex bicycloenediyne nature of this natural product affects it.

References

1. N. Darby, C.U. Kim, J.A. Salaün, K.W. Shelton, S. Takada, and S. Masamune, "Concerning the 1,5-didehydro[10]annulene system", J. Chem. Soc. D, vol. 0, pp. 1516-1517, 1971. https://doi.org/10.1039/c29710001516
2. R.R. Jones, and R.G. Bergman, "p-Benzyne. Generation as an intermediate in a thermal isomerization reaction and trapping evidence for the 1,4-benzenediyl structure", Journal of the American Chemical Society, vol. 94, pp. 660-661, 1972. https://doi.org/10.1021/ja00757a071
3. R.K. Mohamed, P.W. Peterson, and I.V. Alabugin, "Concerted Reactions That Produce Diradicals and Zwitterions: Electronic, Steric, Conformational, and Kinetic Control of Cycloaromatization Processes", Chemical Reviews, vol. 113, pp. 7089-7129, 2013. https://doi.org/10.1021/cr4000682
4. H. Rzepa, "Mechanism of the Masamune-Bergman reaction", 2024. https://doi.org/10.14469/hpc/14546
5. K.C. Nicolaou, G. Zuccarello, C. Riemer, V.A. Estevez, and W.M. Dai, "Design, synthesis, and study of simple monocyclic conjugated enediynes. The 10-membered ring enediyne moiety of the enediyne anticancer antibiotics", Journal of the American Chemical Society, vol. 114, pp. 7360-7371, 1992. https://doi.org/10.1021/ja00045a005
6. E.M. Greer, C.V. Cosgriff, and C. Doubleday, "Computational Evidence for Heavy-Atom Tunneling in the Bergman Cyclization of a 10-Membered-Ring Enediyne", Journal of the American Chemical Society, vol. 135, pp. 10194-10197, 2013. https://doi.org/10.1021/ja402445a

Back in 2017, I was asked to peer review an article and its author asked if I would like the review to be “open” – that is that my name would be shown as a reviewer; [1] Replication in Science was – and still is – a hot topic and I had taken the opportunity with this article to try to (successfully I might add) replicate its main (computational) findings. This is something relatively easy to do with computation, but of course far more of challenge to do for experimental work for obvious reasons. I still regularly attempt some level of replication when I review articles nowadays.

So on to 2024, when I was asked – this time as an author – whether I would like the reviews of our own article to be so included, [2] now called transparent peer review.

Now the open aspects have been inverted! Whereas the identity of the reviewers continues to be withheld, their actual reviews are now available to be read, along with the authors’ responses. There is still no way in which any attempt at “replication” can be indicated – the reviews themselves are in free-text form and the reader has to judge for themselves what they might mean and whether replication was part of the process. I also wonder if replication whilst preserving reviewer anonymity can be achieved?

Not all journals by the Royal Society of Chemistry publisher offer transparent review and it is optional of course. But a search of the string “To support increased transparency, we offer authors the option to publish the peer review history alongside their article” suggests around 73 articles in several journals have such review. What is more difficult to establish is what proportion of published articles expose their reviews – is it a high or a low percentage?  Time will probably reveal this aspect.

It is also worth noting another experiment along these lines, the so-called Octopus publishing[3] model, where a scholarly article can have up to eight distinct components, each in theory written by different authors and where any one section could have several contributions –  including a replication study. Each set of authors gets credit, in the form of one or more publication DOIs. This publishing experiment has been running now for almost four years, although I note there are few if any submissions in the area of physical sciences and chemistry.

It might be fair to suggest that with innovations such as these, scholarly publishing is likely to evolve significantly over the next few years.

References

1. D.C. Braddock, S. Lee, and H.S. Rzepa, "Modelling kinetic isotope effects for Swern oxidation using DFT-based transition state theory", Digital Discovery, vol. 3, pp. 1496-1508, 2024. https://doi.org/10.1039/d3dd00246b
2. H. Rzepa, "Octopus publishing: dis-assembling the research article into eight components.", 2021. https://doi.org/10.59350/qxjaz-a2298

Metadata is something that goes on behind the scenes and is rarely of concern to either author or readers of scientific articles. Here I tell a story where it has rather greater exposure. For journals in science and chemistry, each article published has a corresponding metadata record, associated with the persistent identifier of the article and known to most as its DOI. The metadata contains information about the article such as its authors and their affiliations, the title of the article and its abstract, and is submitted to/registered with Crossref –  an organisation set up in 1999 on behalf of publishers, libraries, research institutions and funders. Relatively recent additions to Crossref metadata are the citations included in the article, so-called Open Citations. Doing so has helped to create the new area of article metrics, used by e.g. Altmetrics or Dimensions  to help identify the impacts that science publications have. Basically, if one article is cited by another, it is making an impact. Many citations of a given article by other articles means a larger impact. Most researchers love to have a high – and of course positive – impact and perhaps for better or worse, academic careers to some extent depend on such impacts.

With that as the background, I now move to a recent article of ours.[1] The metadata record for this article can be obtained using the query:
https://api.crossref.org/works/10.1039/D3DD00246B/transform/application/vnd.crossref.unixsd+xml (retrieved 14/07/2024).^‡

This has 63 citations in the body of the article, with the unusual but pertinent aspect that 30 of these relate not to other articles or to web links, but to data – specifically FAIR data. We even comment on this in our conclusions – “The citations noted here are included in the metadata record for the article, which is registered with Crossref, albeit with one significant current limitation in that there is currently no formal declaration of these citations as specific pointers to a FAIR data collection.” This statement was made on the premise that the article citations would show a 1:1 match with the metadata entries (which they do, see below.  But see also here[2]).

Before I take a look at this, I note that CrossRef metadata does not treat all citations equally. The traditional form of citation appears as such for reference 25 (there are 29 of these in total).

<citation key="D3DD00246B/cit25/1">
<journal_title>J. Chem. Phys.</journal_title>
<author>Scalmani</author>
<cYear>2010</cYear>
<first_page>114110</first_page>
<doi>10.1063/1.3359469</doi>
</citation>

A variation of this is used for variations on journal articles such as preprints, where an “unstructured” component is added to the citation. This is often used as a short commentary added by the authors relating to the citation  – in this case indicating that it relates to a preprint of the article itself. The term “unstructured” also means that the commentary may not have any predictable patterns, or use any terms from a specified dictionary,  and may need the special expertise of a human to process it. In other words, “unstructured” components may not be “machine friendly”. Or that a machine may have to work quite hard to work out what to do about the commentary.

<citation key="D3DD00246B/cit10/1">
<volume_title>ChemRxiv</volume_title>
<author>Braddock</author>
<cYear>2024</cYear>
<doi>10.26434/chemrxiv-2023-vcmcl</doi>
<unstructured_citation>For a preprint, see, D. C.Braddock, S.Lee and H. S.Rzepa, SWERN Oxidation. 
transition structure Theory is OK, ChemRxiv, 2023, preprint, 10.26434/chemrxiv-2023-vcmcl
</unstructured_citation>
</citation>

A third variation on this is present, but this time apparently relating to data itself.  Note again the use of an “unstructured” commentary, which effectively adds the information that the citation might “apparently” relate to data. To be fair,  the volume title also does that, but this should not be its job!

<citation key="D3DD00246B/cit19/1">
<volume_title>Imperial College Research Data Repository</volume_title>
<author>Braddock</author>
<cYear>2023</cYear>
<doi>10.14469/hpc/13108</doi>
<unstructured_citation>
D. C.Braddock , H. S.Rzepa and S.Lee, Imperial College Research Data Repository, 2023, 
10.14469/hpc/13108</unstructured_citation>
</citation>

Why might this be important? Well, the mantra nowadays is that information has to be processable not only by humans but also by machines undertaking learning or “artificial intelligence. Such ML/AI is at least in part about finding predictable patterns in data, and unstructured citations imply a certain lack of predictability! A machine can “read” a journal article  and that should also be possible for the data on which inferences reported in the article are made. So that data has to be accessible in the first instance and then interoperable and re-useable in the second instance. These attributes are known as FAIR. So it would be great if the metadata for the article could indicate to a machine when associated data might be available – and even better to suggest that this data might have attributes of FAIR.

So we now understand that there  does need to be a formal agreed way of specifically expressing a data citation in the CrossRef metadata, rather than just carrying an unstructured commentary in the citation. The good news is that such is on the way! A public discussion document requests comments by August 15th, 2024  and introduces two  new Crossref additions to the metadata, which are interpreted below in terms of the article we are discussing.

1. <citation  type=”dataset” key="D3DD00246B/cit19/1">
   <volume_title>Imperial College Research Data Repository</volume_title>
   <author>Braddock</author>
   <cYear>2023</cYear>
   <doi>10.14469/hpc/13108</doi>
   </citation>

A more formal statement is also now added, and I quote Crossref’s reasons for its inclusion “we’d like to support several types of free-text statements in our metadata records as we’ve had feedback that they can be useful for downstream metadata users who are able to parse out and refine chunks of text in ways that may be useful. The statements are also useful for re-use in some situations.” In some ways, it replaces the unstructured citation from the example above, but now using a controlled dictionary term to specifically relate to data.

2. <statement type=”data availability”>Data Availability and Discovery Statement</statement>

Let us now see how all this is handled for the article we are discussing.[1]

• The data itself, found as a collection with its own metadata record[3] can and does cite the article[1].
• The Crossref metadata record for the article as of 17.07.2024 has 38 entries which include an <unstructured_citation>, including 30 relating to data (which are currently inferred by a human).
• If the metadata changes noted above are implemented, the 30 data citations will be clearly identified as such, as in the example shown in item 1 above, and no human inference would be needed.

The CrossRef public discussion document will remain available for another four weeks or so – meanwhile, public comments are requested! Once these enhancements have been implemented, we hope that the article metadata record we are analysing here can in turn be updated^‡ to reflect the FAIR data richness of the article. And then perhaps Altmetrics or  Dimensions  can start producing metrics relating to the impact of cited data. Watch this space!

^‡One difference between the article itself and its metadata record is that the former does not change {unless a corrigendum is issued} – it is a so-called Version-of-Record or VOR, whereas the metadata record itself can be responsibly updated when deemed necessary. So it is important to note the date associated with any given version of a metadata record.

References

1. D.C. Braddock, S. Lee, and H.S. Rzepa, "Modelling kinetic isotope effects for Swern oxidation using DFT-based transition state theory", Digital Discovery, vol. 3, pp. 1496-1508, 2024. https://doi.org/10.1039/d3dd00246b
2. L. Besançon, G. Cabanac, C. Labbé, and A. Magazinov, "Sneaked references: Cooked reference metadata inflate citation counts", arXiv, 2023. https://doi.org/10.48550/arxiv.2310.02192
3. H. Rzepa, "Modelling kinetic isotope effects for SWERN Oxidation. DFT-based transition structure Theory is OK.", 2023. https://doi.org/10.14469/hpc/13058

I should start by saying that the server on which this blog is posted was set up in June 1993. Although the physical object has been replaced a few times, and had been “virtualised” about 15 years ago, a small number of the underlying software base components may well date way back, perhaps even to 1993. This system had begun to get unreliable in recent years, and it was decided about 6 months ago to build an entirely new virtual server and then migrate stuff to it.

This switch over from old to new servers happened on June 14 2024 and a DNS host tables switch was made to point the URL of the server to the new version rather than the old one. A significant change was to move from php 7 to php 8 and doing this broke a couple of the installed WordPress plugins. The most important was KCite, which handles the referencing of each post. The input is merely the DOI of the reference and Kcite expands this to a full citation and inserts this at the foot of the post. Whilst we wait for a new version of Kcite to appear (which will in fact do a great deal more than the old one), references here will appear merely as [1] for the time being. The other breakage was the ORCID plugin, which inserts the author ORCID into the post.

There are other things broken which will be worked upon over the next few days or so. Initially, images were broken, but it seems this has now been fixed specifically for the invocation https://www.ch.ic.ac.uk/rzepa/blog/?p=27133 rather than https://www.ch.imperial.ac.uk/rzepa/blog/?p=27133, an error caused by internal use of two different host names, one of which had been set up as alias of the other when Imperial College was “rebranded” about 20 years ago from ic.ac.uk to imperial.ac.uk. The new server does not currently like this mixing! Another misbehaving feature is the invocation of Jmol 3D molecular models by image clicking, which produces “Failed to load resource: the server responded with a status of 412 (Precondition Failed)”. Whilst we ponder this, bear with us. Meanwhile, the server is much more responsive and previous 3 minutes pauses in response hopefully are now fixed.

A 31 year old web server and its underlying services is a positive dinosaur in this information age (if anyone has one that has been broadcasting from the same URL for longer, please let me know). Hopefully, now that it has been given a transplant, it will go on for a few years longer.

References

1. https://doi.org/

Chemists now use the term “curly arrows” as a language to describe the electronic rearrangements that occur when a (predominately organic) molecule transforms to another – the so called chemical reaction. It is also used to infer, via valence bond or resonance theory, what the mechanistic implications of that reaction are. It was in this latter context that the very first such usage occured in 1924[1] taking the form of a letter by Robert Robinson to the secretary of the Chemical Society and “read” on December 18th 1924. The following diagram was included:

First curly arrows

I have commented previously on this diagram[cite10.59350/qqwk3-dgj13[/cite] and will not discuss it further here. To commemorate the 100th anniversary of their invention, I include shots of two “modern” sets of curly arrows, taken from a lecture I give to university students at the end of their first university year.

1. The first was a new take on the peracid epoxidation of an alkene[2] in which quantum mechanical calculations have revealed that the classic take on the curly arrow mechanism for this reaction can be split into two sets, five for the first stage of the reaction up to the transition state and two for the final stage

Four becomes seven

2. The second was also discussed here[3] and involves what is arguably a new type of arrow to join the existing stable – the dashed arrow (in red below). This electron transfer arrow can take place over long distances (15Å or more) and adds the concept that an arrow can have the properties of an (approximate) length as well as direction, start+points and perhaps even “curlyness”.

Proton coupled electron transfers

As a “language” describing mechanism and reactivity in molecules, curly arrows are still in common use, but as chemistry itself evolves into new areas, will curly arrows themselves morph into new forms, or will their use gradually decline?

References

1. "Forthcoming events", Journal of the Society of Chemical Industry, vol. 43, pp. 1295-1298, 1924. https://doi.org/10.1002/jctb.5000435208
2. H. Rzepa, "Experimental evidence for "hidden intermediates"? Epoxidation of ethene by peracid.", 2013. https://doi.org/10.59350/fdy9j-9fp48
3. H. Rzepa, "Curly arrows in the 21st Century. Proton-coupled electron transfers.", 2020. https://doi.org/10.59350/rj90z-mxh96

I can remember a time when journal articles carried selected data within their body as e.g. Tables, Figures or Experimental procedures, with the rest consigned to a box of paper deposited (for UK journals) at the British library. Then came ESI or electronic supporting information. Most recently, many journals are now including what is called a “Data availability” statement at the end of an article, which often just cites the ESI, but can increasingly  point to so-called FAIR data. The latter is especially important in the new AI-age (“FAIR is AI-Ready”). One attribute of FAIR data is that it can be associated with a DOI in addition to that assigned to the article itself, and we have been promoting the inclusion of that Data DOI in the citation list of the article.[1] Since the data can also cite the article, a bidirectional link between data and article is established. ESI itself can exceed 1000 “pages” of a PDF document and examples of chemical FAIR data exceeding 62 Gbytes[2] (Also see DOI: 10.14469/hpc/10386) are known. Finding the chemical needle in that data haystack can become a serious problem. So here I illustrate a recent suggestion for moving to the next stage, namely the inclusion of a “Data Availability and Discovery” statement. The below is the text of such a statement in a recently published article.[3]

Data availability and discovery statement. Available as a FAIR and AI-ready data collection accessible via doi: 10.14469/hpc/13058 for the overall collection¹⁸ and Findable by following the hierarchy of data collections identified there. The data discovery and accessibility aspects are further enabled by using one of the following methods.

•  Discovery using a metadata search query. A example template for identifying kinetic isotope data deriving from a  Gaussian calculation is here provided as: https://commons.datacite.org/?query=media.media_type:chemical/x-gaussian-log+AND+media.media_type:text/plain+AND+(titles.title:*Exo*+OR+titles.title:*Endo*) This query can be modified or extended as required.
• Discovery using an enhancement of Table 1,⁵⁵ doi: 10.14469/hpc/13370 acting as a data Finding Aid via links to data collections associated with each row of Table 1.  A visual tool for displaying interactive 3D coordinate models illustrating the transition structure normal vibrational modes is interactively created by re-using data accessed and exploiting information in the metadata record associated with the FAIR doi of each dataset.

Many variations on the above search can be constructed[4] It is also useful to note that the above syntax presents the results of the search in “human readable” form. For a machine version, either of the two forms below should be used.

1. https://api.datacite.org/dois/?query=media.media_type:chemical/x-gaussian-log+AND+media.media_type:text/plain+AND+(titles.title:*Exo*+OR+titles.title:*Endo*)
2. curl "https://api.datacite.org/dois/?query=media.media_type:chemical/x-gaussian-log+AND+media.media_type:text/plain+AND+(titles.title:*Exo*+OR+titles.title:*Endo*)"

These last forms emphasise that data discovery is aimed at machine automation as well as humans.

Finally, I ponder how machines will respond to articles containing references to such discoverability. Ideally, the machine actionable information should itself be included in the (CrossRef) metadata describing the article. At the moment that aspect is perhaps the weakest point of machine discoverability associated with journals.

References

1. H. Rzepa, "The journey from Journal "ESI" to FAIR data objects: An eighteen year old (continuing) experiment.", 2023. https://doi.org/10.59350/g2p77-78m14
2. T. Mies, A.J.P. White, H.S. Rzepa, L. Barluzzi, M. Devgan, R.A. Layfield, and A.G.M. Barrett, "Syntheses and Characterization of Main Group, Transition Metal, Lanthanide, and Actinide Complexes of Bidentate Acylpyrazolone Ligands", Inorganic Chemistry, vol. 62, pp. 13253-13276, 2023. https://doi.org/10.1021/acs.inorgchem.3c01506
3. D.C. Braddock, S. Lee, and H.S. Rzepa, "Modelling kinetic isotope effects for Swern oxidation using DFT-based transition state theory", Digital Discovery, vol. 3, pp. 1496-1508, 2024. https://doi.org/10.1039/d3dd00246b
4. H. Rzepa, "A cascading tutorial in finding rich NMR data using the Datacite datasearch engine.", 2020. https://doi.org/10.59350/7jq8v-z4p56