While scientific companies frequently publish their research in academic journals, it seems broadly true that publication is not incentivized for companies the same way it is for academic groups. Professors need publications to get tenure, graduate students need publications to graduate, postdocs need publications to get jobs, and research groups need publications to win grants. So the incentives of everyone in the academic system are aligned towards publishing papers, and lots of papers get published.
In contrast, the success or failure of a private company is—to a first approximation—unrelated to its publication record. Indeed, publication might even be harmful for companies, insofar as time spent preparing manuscripts and acquiring data only needed for publication is time that could be spent on more mission-critical activities.
That’s why I generally believe industry publications, especially those where no academic co-authors are involved, are underrated, and are probably better than the journal they’re in might indicate. Getting a publication into a prestigious journal like Science or Nature is pretty random, requires a lot of effort, and frequently has a slow turnaround time, whereas lower-tier journals are likely to accept your work, and typically review and publish papers much, much faster. (In particular, ACS is among the fastest of all scientific publishers, and is generally a pleasure to work with.)
The above reflections were prompted by reading an absolute gem of a paper in J. Med. Chem., a collaboration between X-Chem, ZebiAI, and Google Research. The paper is entitled “Machine Learning on DNA-Encoded Libraries: A New Paradigm for Hit Finding” and describes how data from DNA-encoded libraries (DELs) can be used to train ML models to predict commercially available compounds with activity against a given target. This is a really, really big deal. As the authors put it in their conclusion:
[Our approach] avoids the time-consuming and expensive process of building new chemical matter into a DEL library and performing new selections or incorporating new molecules into a HTS screening library. This ability to consider compounds outside of the DEL is the biggest advantage of our approach; notably, this approach can be used at a fraction of the cost of a traditional DEL screening follow-up, driven primarily by the large difference in synthesis cost.
Now, the precise impact of this discovery will of course be determined in the years to come; Derek Lowe raises some fair concerns on his blog, pointing out that the targets chosen are relatively easy to drug, and so probably wouldn’t be the subject of a high-tech DEL screen anyway, and it’s entirely possible that there will be other unforeseen complications with this technology that are only revealed in the context of a real-world discovery pipeline. (Given that Relay acquired ZebiAI for $85M in 2021 essentially on the strength of this paper alone, I’m guessing plenty of real-world testing is already underway.)
The point I want to make is that if this paper had come from an academic group, I would be very, very surprised to see it in J. Med Chem. This project has everything that one expects in a Science paper: a flashy new piece of technology, a problem that’s understandable to a broad audience, clear clincal relevance, even a domain arbitrage angle. Yet this paper is not in Science, nor ACS Central Science, nor even JACS, but in J. Med. Chem., a journal I don’t even read regularly.
My conclusions from this are (1) to remember that not everyone is incentivized to market their own findings as strongly as academics are and (2) to try and look out for less-hyped industry results that I might neglect otherwise.