It’s an interesting idea, but we think our unique advantage is being able to generate scalable,, relatable, and reliable data in-house. Clinical data are extremely challenging to work with from a statistical perspective (the number of confounders is astounding, and once you stratify you may be left with very few samples). That said, real-world evidence is certainly interesting from a clinical development perspective for understanding the patient landscape, longitudinal disease progression, and for informing patient selection strategies in clinical trials; and other population-scale datasets may be of interest for advancing our discovery and development pipelines.

This is why we don’t just take the inferences from our maps of biology and send them into clinical trials. The FDA has a lot of useful restrictions on testing drugs in humans that ensure that everyone does a ton of work to minimize risk of experimenting in humans. For example, we do numerous validation experiments in human cells, animal models and preclinical models after our AI gives us input but before we go into trials and many of these experiments address safety. That said, one can never minimize risk to zero and we take our responsibility to patients seriously.

How can we cure muscular dystrophy!?

We have a vibrant innovation arm and we actively seek opportunities to enhance the use of our data to decode biology and develop therapeutics for patients. While we can’t comment on the specifics of our explorative biology and tech, metagenomics is certainly in the spirit of the work we do.

As a computational proteomics researcher who works mostly in MS, it feels like there are dozens more transcriptomics colleagues around me per metabolomics/proteomics person lol Though there are definitely exciting developments in high throughput technologies, even at single cell scale, coming up.

We certainly protect and will continue to protect our development candidates using industry standard kinds of patent filings. But, as you imply, our development candidates are only a small part of the innovation that happens at Recursion. We do have multiple patents and filings on our RecursionOS, but we also look at protecting inventions in the biology and hardware spaces where we innovate. We also protect some of the key advances on our platform via trade secret. This doesn’t even take into account the massive amount of proprietary data we’ve generated.
That said, we think we can contribute a lot to open-science without giving away our advantage - see [our RxRx datasets](https://www.rxrx.ai/) and [publications](https://www.recursion.com/scientificmaterials).

Can this technology lead to customized healthcare on a per individual level?

Can you take someone’s genetic info, run it through the AI and pinpoint which medications would be best for that individual and/or synthesize new medications that would work best for that one person?

I’m an investor of both companies. I’ve been working my cost avg down on RXRX. And my cost avg on MRNA is moving up. I still believe in both platforms and have yet to sell any stock of either company. The future is bright for both of you. God speed.

Directing evolution of bacteria to change their small molecule output is indeed a great example of the utility of AI and is definitely similar to how we view AI in the overall evolution of a compound series. Today, our core applications of AI are at a lower level in the stack – for example, taking raw images from our microscopes and projecting them into biologically meaningful embedding spaces. That said, we’re building our discovery technologies with an eye towards building closed-loop optimization cycles in small-molecule discovery. We actually just presented more about this a couple weeks ago – if you’re curious, see more here in the Recursion OS section from our recent Download Day.

Q1: What is your opinion opensourcing the full dataset? and the possible benefits for medicine of doing so.

Q2: What is your biggest struggle at this moment to go to the next level?

Have you looked at using Conformal Prediction for uncertainty quantification in your ML pipeline? If so why not? It's a technique that has been around for a while but I don't see it massively widely used, though some of the example use cases I have seen were drug discovery NNs.

That’s like asking us to choose a favorite child… can’t say.

What processes do you have for model and data evaluation? Tangential - have you found a way to use synthetic data generation to train or validate your models?

It's great that large datasets are being pulled in, but what are procedures around making sure that the data itself is good/useful?

I have genetics on the brain, so yes: I definitely think that data from both germline GWAS and somatic variation studies can be valuable for drug discovery. We don’t work on antibodies at Recursion today (though we have piloted them and they worked great on the platform), but we certainly make use of genetics data to inform our directions. As far as canonical targets, our platform allows us to be agnostic and to explore without having to select a target. As we move through our drug discovery process we aim to understand as much as possible about the target and its mechanism of action.

This is not an area we are working on, but we think it is really important. We founded a biotech and healthcare incubator called [Altitude Lab](https://altitudelab.org) to help grow the next Recursion and support underrepresented founders here in the Mountain West, and there is a young company there working on this exact problem.

Have you'll experimented with single cell Multiomic platforms like 10x or Missionbio?

The major benefit that I see with single cell data is that it provides clonal information which aren't available in bulk methods. Do you see any benefits of these technologies in drug discovery? Can they help improve your models?

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We don’t sell software. Check out a demo of one of our internal tools, [MolRec](https://www.rxrx.ai/molrec). We don’t collect data from other laboratories but we do partner closely with select drug discovery partners.

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It looks like the cloud. It also looks like BioHive-1, our private supercomputer (#115 in the world on the latest TOP500 list).

The scale of data required to understand biology, paired with our susceptibility to bias as humans, is a big limiting factor on our (useful) creativity in biology. Augmenting our team with less biased ML and AI systems to explore the complexity of biology and chemistry is a recipe for success for increasing creativity IMO.

Though there have been a lot of painful layoffs in biotech and tech lately, we and many other companies are still hiring. That said, computational chemistry is without a doubt going to be a critical component of the future of drug discovery and it’s awesome you’re kicking off your career in this space. We will certainly be continuing to grow in this space and would love to hear more about your work and journey in this field. As you can probably tell, we look to hire innovators who are passionate about their work and committed to bold, outside the box thinking in pursuit of our mission.

We asked ChatGPT…
It says: “Recursion Pharmaceuticals uses artificial intelligence as a tool to discover new medicines, but its AI is not similar to ChatGPT. ChatGPT is a language generation AI model that can generate human-like text based on input data. In contrast, Recursion Pharmaceuticals uses AI for image analysis and high-throughput screening to identify new drug targets and develop new treatments for diseases. The AI used by Recursion Pharmaceuticals is more specialized and focused on drug discovery, while ChatGPT is a more general-purpose language generation AI model.”

Thanks ChatGPT!

Why the decision to ignore structure based drug design?