The meeting started, as usual, wth the CSA Trust Mike Lynch Award, this year awarded to Prof. Val Gillet (see also this press release).

This time, there where the following themes, where the first was by far the most dominant theme:

• Artificial Intelligence, Machine Learning, and QSAR (five sessions)
• New Modalities and Large Chemical Data Sets (one session)
• Advanced Cheminformatics Techniques (two sessions)
• Integrative Structure-Based Drug Design (two sessions)

My contribution this time was a poster for the VHP4Safety project, but more about that later.

Like last time, I have been annotating speakers with identifier and accounts, if they provided those:

As you can see, it also includes PDFs, for both talks and posters. At the time of writing, I collected PDFs of two presentations and five posters. Additions are still most welcome, ideally with DOI, so that they can be cited (doi:10.5281/zenodo.15494630, doi:10.13140/RG.2.2.27441.90720 doi:10.5281/zenodo.15614295, and doi:10.13140/RG.2.2.36774.23365)!

Finally, I like to remind everyone that there is again a proceedings collection in the Journal of Cheminformatics, and presenters of oral and poster presentations are invited to submit their presented work to this collection.

In turn, that poster described work of at least three years, going back to adding identifiers in 2015 and chemical structures in early 2016. I started using scripts two months later. This helped a lot with migrating pKa data from a custom Semantic MediaWiki installation to Wikidata and with adding thousands of EPA CompTox identifiers in 2017.

But that 2018 conference paper never happened. Because Scholia did. And even on the ICCS poster, Scholia was used to visualize chemistry data in Wikidata. To be honest, not just that, of course. About a year ago I had a serious go at finishing the paper, and it was sent around to co-authors. But I realized at the time, that the paper was lacking some good suggestions how the peer review our actual contributions to Wikidata. I could hardly expect readers of the paper browse the individual histories of all, by then, 1.3 million chemical compounds. And during the holidays I collected a few tools, which I had lined up to add to the manuscript.

However, another thing happened, the COVID-19 pandemic. While all the experience helped a lot with getting knowledge together around SARS-CoV-2, it also made something else clear: the software behind Wikidata does not scale well (enough). This lead to plans to split the RDF graph representation into two separate SPARQL endpoints. And that breaks many, if not most, of Scholia’s SPARQL queries, including those for the chemistry aspects. The situation in Summer 2024 was that there was a significant chance Scholia would not survive the split. And the Scholia Chemistry paper had to wait. You cannot publish an article of which the website is gone before it is formally accepted.

Let me make clear, this graph split is not solved and the risk is not gone. But a serious of unfunded, weekend hackathons allows us to refactor Scholia to give us a chance. It started with making Scholia more configurable. We had the first hackathons in October and November, and I had four more hackathon weekends this April.

The graph split into a main graph and a scholarly graph happened on May 9. Currently, we have been granted extra time and can use a legacy server with the full graph, but a lot less hardware, so slower. A final patch, merged in last week, allows us to define which SPARQL endpoint a query should run. So, each time we port a SPARQL query, we can directly update Scholia, making the migration somewhat more manageable.

But, with those uncertainties out of the way, it was time to finish the Scholia Chemistry paper!

The preprint (doi:10.26434/chemrxiv-2025-53n0w) brings 10 years of research together, and describes details of the used methods not formally peer-reviewed before. We describe in detail how chemical structures are added, the choices of Wikidata on how to represent chemical structures, how we curate the quality, and how we visualize chemical structures and data with Scholia. As you can expect, the Chemistry Development Kit has an important role, along with the InChI.

The paper introduces three new Scholia aspects for chemicals, chemical classes, and elements. Each aspect is a template for a page with information about molecular entities and chemical substances, compound classes (like fatty acids), and elements (like carbon). Each template provides relevant information. Of course, any compound, class, or element can also still be opened in the Scholia “topic” aspect, listing relevant literature.

With this paper we aim to show that Wikidata is a innovative platform that meets the needs for a chemical structure database, with detailed data provenance, and scalable community curation.

I welcome your strongest peer review on the preprint. I don’t liking settling for anything less. Here’s the abstract:

Sharing knowledge on chemicals in the digital age has been the playground of databases such as the Chemical Abstract Services and PubChem. Wikipedia complements this field by providing context to chemicals aimed at a broad audience, but is not easily read by machines. Wikidata was started as a database service to improve the machine readability of the knowledge captured in Wikipedia. Wikidata has an open license, application programming interfaces, and a strong provenance model. Scholia uses the features to provide access to chemical knowledge. This study reviews the chemistry in Wikidata, shows how thousands of new chemicals were added, extends Wikidata with new properties for chemical representation and external links to additional databases, and shows how we extended Scholia to represent the chemistry in Wikidata.

Thanks to Finn, Denise, Daniel, and Adriano!

Part of the scientific program of the ICCS 2022.

The conference will have 5 sessions:

• Analysis of Large Chemical Data Sets,
• Structure-Activity and Structure-Property Prediction,
• Dealing with Biological Complexity,
• Structure-Based Approaches, and
• Cheminformatics Approaches.

I am also a bit disappointed that the nanoinformatics community did not submit much work. I guess that like the gaps with the nanomedicine community, the link with the cheminformatics community is also a bit weak.

I am also delighted that two BiGCaT researchers (Denise Slenter and Ammar Ammar) will present their research in Noordwijkerhout. I am looking forward to seeing you all in June. You can register here. Keep in mind the number of places is limited and the registration is filling up quickly.

Later this year the ninth International Conference on Chemical Structures (ICSS) conference will be held in the Netherlands. I had the pleasure of joining this meeting, I think, eight years ago, when I was doing my PhD in Nijmegen. Mind you, I did not attend the conference; I helped with the organization ;) That was a good deal, particularly because I got to meet many cheminformaticians while working behind the registration desk ;)

Actually, my gravatar still reflects that meeting, as it is a picture taken on the boat trip on the Markermeer. That was one great boat trip: I steered a driemaster, and helped out on the boat on ropes outside the deck, meters above the water. Cheminformatics can be so nice! The photo was taken during a calmer part of that boat trip :)

Back to the ICCS. It’s one of the bigger cheminformatics meetings, and likely the best after the yearly GCC meetings. Mind you, the term cheminformatics reflects more the methods than the domains. Indeed, the meeting’s Call for Papers lists many topics highly relevant to my position here at KI, including chemogenomics, (Q)SAR, literature mining, “integration of medical and biological information” (including semantic web technologies), and in-silico analysis of toxicology, drug safety, and adverse events.

Depending on the schedule this year, I may actually submit an abstract based on what we will do in the next year, and see what happens. The CfP deadline is 31 January.

Later I encountered MoSS which has the same goal, but uses a different algorithm. MoSS is written in Java and uses the LGPL license. MoSS reads STN and SMILES as input, which might not be optimal for all users, so a CDK port comes to mind.