Well, sort-of-kind-of peer-reviewed. The publication in question went through several peer-review rounds before we decided (due to lack of time and resources) to go with ChemRxiv and leave it at that. But at least it’s out there.
Here is the link: https://doi.org/10.26434/chemrxiv.12613214.v1
Machine Learning to Reduce Reaction Optimization Lead Time – Proof of Concept with Suzuki, Negishi and Buchwald-Hartwig Cross-Coupling Reactions
Fernando Huerta, Samuel Hallinder, Alexander Minidis
We are continuing our work on this, but will probably take it in slightly different direction.
Noteworthy is that just around the same time, we had a my work place, RISE AB, Södertälje, Sweden, an international online workshop on the topic:
Accelerating chemical design and synthesis using artificial intelligence
The presentations are available as binder with ISBN: 9789189167421
As a bit of whining part (it’s my blog after all), regarding the paper, I have to say, it’s been a rough ride – we starting writing fall 2019 and were ready just before XMas. But while the peer-reviewers had some good points, there were some aspect were we felt they “didn’t get it” (meaning maybe we weren’t very clear). They seemed to also judge the paper not from the mixed audience view, something we admittedly struggled with. And after we received questions that had been answered twice(!) in earlier revisions and comments that have nothing to do with a publication we figured that we secure our publication date via ChemRxiv. Because, to be honest, we had the uneasy feeling that some of the reviewers might not be 100% ethical and use some of our ideas. And we believe that has happened. There is a ChemRxiv publication of an expert in the field who used an eerily similar conceptual idea – Pd-catalyzed cross-coupling and splitting the data set in “good” and “bad” to obtain better separation. I am paraphrasing here, but some wordings just in the abstract alone were…. Anyway, that could be coincidence of course. So let’s leave it at that.