No big deal? Dual use of artificial-intelligence-powered drug discovery

A few months ago a paper was published discussing the abuse potential of modern AI based drug models and how it could lead to deliberately ill intended toxic compounds. The article itself can be found here: Dual use of artificial-intelligence-powered drug discovery | Nature (unfortunately behind a pay-wall).

(image created by Crayon using “artificial intelligence creating drugs”)

I didn’t participate at the conference from where this paper evidently originated from, so I don’t know how the presentation or discussion went leading to this article. Would have been interesting though to have participated, because it’s rather surprising to me, that “all of a sudden” the whole abuse potential would be so surprising.

“By inverting the use of our machine learning models, we had transformed our innocuous generative model from a helpful tool of medicine to a generator of likely deadly molecules.” (Ekins et al., Nature article)

Shortly after said publication, Lowe wrote a commentary in Science: Deliberately Optimizing for Harm | Science (fully accessible). He agrees along the lines of the abuse potential of “inverting of the model”, i.e., instead of finding “good, treatment based” drugs, one uses it to find “bad, toxic” compounds, but he isn’t as worried (at this stage) and I concur; though perhaps for somewhat different reasons?

“…..I’m not surprised at all that computational methods can point the way to such things, although I can see how the authors of this work found demonstrating this to be an unsettling experience. To be honest, I’m not all that worried about new nerve agents, ………That is, I’m not sure that anyone needs to deploy a new compound in order to wreak havoc – they can save themselves a lot of trouble by just making Sarin or VX, God help us…..” (Lowe, Science)

Honestly, I think the whole publication is extremely exaggerated and not such a big deal, almost to the degree where I wonder if it was written more to create hype than awareness? Is it perhaps even an unnecessary publication? Perhaps not. But i digress –

Why isn’t this such a big deal? Well, simply because all the tools have been out there for years that allow you to come up with toxic compounds if so desired – without the use of any advanced modelling techniques.

Myself, I mentioned along similar lines already in 2017 here in a short blog about the abuse potential of open-access: Abuse of open access tools and data . I wasn’t even the first to have these observations (I cite a Guardian article from 2013….), and I am certain this has been discussed and published elsewhere. And it’s perhaps what Lowe refers to with “just go and wreak havoc with a known compound”.

Let’s have a somewhat different look at this topic:

To be able to use these modern AI tools and actually turn it into anything, no matter if good or bad, you require a bit more knowledge than just downloading a model from Github. But, should you do have some chemical/pharmaceutical know-how, then you are probably much easier off using open-access such as PubChem and look for a target with known safety issues. If we use pharmaceutical discovery as reference, there is a whole battery of known “no-no targets” which an evil person or organization could pick from. Any potent compound for any such a target, especially if it hits several, is a potential poison! No need for modelling! Most often than not such a multi-target compound would be even “better” (well, worse, in this case). Finally, even if there are public models for any given target, if you have evil intent, you won’t bother considering the effort. It might be a bit more of an effort if you have specific distribution method in mind – in someones drink? A bullet in a home-brewn umbrella gun? Etc. Etc.

The only exception that I (perhaps naïve?) can see would perhaps be in the field of sports with performance enhancing drugs (i.e. doping), since there is a lot of money and advanced knowledge while being in constant race with the authorities. There you would want a “clean” drug, not (easily) detectable and you are down to “classical” drug discovery and the time and resources it so far takes.

If we want to go so far to claim that governmental intelligence agencies are interested in toxins, well, yes, they too would have the means and resources and could use AI systems.

What about illegal recreational drugs? Wouldn’t that be the same? I dare to say no, they usually don’t care about drug quality, they would most likely prefer quick and dirty. And a scraping of Pubchem would probably just do that sufficiently. It seems unlikely that for them selectivity or potency would matter – as long as a certain desired effect is achievable in a cheap fashion.

Here, if any AI modeling would be of an issue, it would be the synthesis tools – how to make a compound effectively in as few steps as possible (i.e. cheap….). Or how to exchange that banned starting material, without having too much expert chemistry knowledge. Having said that, if they target a previously published compound, then the synthesis is already out there, most likely in sufficiently “quick and dirty” manner. For better or worse, with SciHub you can even access most relevant synthesis publications. You might not even need that, a lot is available in freely available supplementary material or patents – as a an expert in the art would most likely know. They wouldn’t care if a paper is illegal to download, or not, that would be the least of their worries in this context. To take another quote from Lowe regarding constraints by ethics or law: “….history demonstrates that anyone truly interested in using such things will care nothing for these constraints.”

To close off this topic, when it comes to the the sole purpose of ill intent: The ones with such an intent and sufficient resources, they have most likely already used their know-how for decades even without AI. With regards to the (not so) far future though – now that that is admittedly a different story yet to be told.

Sweet, another publication! Machine Learning in Reaction predictions!

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.

ChemRxiv

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

Workshop RISE

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.