We’re Recursion and we’re using AI to decode biology and industrialize drug discovery!

What evidence exists that the insights gained via single-cell perturbations can help uncover novel disease targets? A critic might say a single cell perturbations are simply not a good model for complex multicellular disease processes as the disease phenotype is rarely a linear sum of single cell phenotypes. Is the method most applicable to rare diseases with a clearly understood gene driver or also to highly prevalent diseases? I think Yumanity failed recently with their yeast disease model in neurology so I’m curious of how you address this criticism

All reductions of complex biology cut out some of the information and become poorer representations of the patient. Scale and translation are opposing forces in biological experimentation. The most translational model is human - which is hardest to scale. The least translational model is in silico, but is easiest to scale.

What we do at Recursion is work in a human cell, the smallest unit of biology that has all of the instructions. It is not perfectly translational, but there are many examples of where it has worked well. But it does allow us to scale across biology and chemistry (whole genome scale, ~1M compounds, etc).

Using that model, we find the strong correlates of gene function and patient biology from the world’s knowledge of disease, and explore those in our dataset to find ways of modifying those processes. We then do the rigorous work of translating success from our cellular models in much more complex systems. Our clinical programs demonstrate that we are able to confirm these insights from the platform in more complex in vivo models.

which outcome provides the most scientific benefit?

which one contributes more to our collective brain?

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the millions of simulations that fail

or

the one that solves the problem

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wasn't viagra a hair loss drug with an "unfortunate" yet common side effect identified during trials :P

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is the AI looking for "alternative uses"?

Love that we have one of our first questions even before the official start. Honestly, the millions of simulations that fail enable the one that solves the problem. Both matter!

…and yes, Viagra was a drug originally developed for hypertension and angina pectoris, and as the story goes, when the drug didn’t work that well for those indications and they stopped the trials, none of the participants wanted to give back their clinical trial drugs…. because, well, you know…

But counting on serendipity to give us outcomes like that, in diseases of higher unmet need of course, is not a recipe for success. So we’ve created Recursion to systemize serendipity. But we aren’t stopping at known drugs… we’ve built a dataset spanning over a million molecules that could help us find totally new drugs for many diseases. So its alternative uses, new uses, unexpected uses, and more.

My super fun lawyer would want me to also say: this discussion may contain forward looking statements that are based on current day estimates and operations and importantly are subject to a number of risks. For more details please see the "Risk Factors" in our 10-Q and 10-K SEC filings.

EDIT: added link to comment

They didn’t stop the trials mate.

Viagra was brought to market first for Pulmobary Hypertension, and is still on the market for that indication.

After release reports showed massive benefit in ED, this approval for that second indication was obtained.

It is still the major treatment option for pulmonary hypertension an otherwise very quickly lethal disease and now progression can be delayed by decades at best.

Please see the following paper with many helpful refs (https://www.nature.com/articles/nrd1468). Since it is behind a paywall, here's the relevant bit...

"Pfizer was seeking a drug for angina when it originally created sildenafil (Viagra) in the 1980s. As an inhibitor of phosphodiesterase-5 (PDE5), sildenafil was intended to relax coronary arteries and therefore allow greater coronary blood flow. The desired cardiovascular effects were not observed on the healthy volunteers tested at the Sandwich, England, R&D facility in 1991–1992. However, several volunteers reported in their questionnaires that they had had unusually strong and persistent erections. Pfizer researchers did not immediately realize that they had a blockbuster on their hands, but when a member of the team read a report that identified PDE5 as a key enzyme in the biochemical pathway mediating erections, a trial in impotent men was quickly set up. A large-scale study carried out on 3,700 men worldwide with erectile dysfunction between 1993 and 1995 confirmed that it was effective in 63% of men tested with the lowest dose level and in 82% of men tested with the highest dose. Of note, in many of these studies, Pfizer’s researchers had difficulties retrieving unused sample of the drug from many subjects in the experimental group as they did not want to give the pills back! By 2003, sildenafil had annual sales of US $1.88 billion and nearly 8 million men were taking sildenafil in the United States alone."

Sildenafil was approved for ED in the US in 1998, but was later approved for pulmonary hypertension in the US 2005.

1) What is your reason behind not hosting quartly Earning Calls to adress and expand on certain topics together with analysts and make them available on your website/youtube?

2) Are you planning to repeat the Recursion Download Day as a yearly event?

We don’t currently do earnings calls but we like engaging with people where they are, like here on reddit.

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Download Day was a great event! We’re currently thinking we’ll do it every 12-24 months–stay tuned.

Wait you honestly think a reddit AMA is a better use of your time as CEO than actual earnings calls???

Who's your target audience? 14-year-olds with a weekly allowance?

We spend a lot of time with investors and analysts in a wide variety of forums from the JP Morgan Healthcare conference to social media. For example, we recently spent a whole day with our analysts and many key investors digging deep into our strategy, platform, pipeline and partnerships at [Download Day](https://www.recursion.com/download-day). You can watch all four hours of detailed content, including questions from analysts at the link.
We think spending <1% of our time finding creative ways to connect to new audiences is a good use of time. We know there are potential future employees on reddit, potential partners and collaborators and more on here. And if we can inspire a bunch of 14 year olds to use their talents for science, that sounds like a win too.

Hi, and thanks for this AMA.

I've read that AI could be used for reducing the amount of computation necessary to model really complex things like protein folding. Does your work touch on that, or are you otherwise able to comment on whether or not that's true?

In general, how much success have you had in "predicting the result of experiments before we do them"?

Lastly, while I realize you're a company and seeking to make money, do you have any standards in place that you're committed to to avoid price gouging people and/or taxpayers for access to the results of your healthcare-related research?

Thank you for the questions!
AI has made huge inroads into tough problems like protein folding. Huge credit to Deepmind and so many others there!
We’ve gone after a different problem than AlphaFold (and others). Can we understand the function of all the proteins in our body without necessarily needing to know the structure? If one could understand cause and effect of all the proteins (when they are overactive, not present, or broken, etc), we could start to better understand what protein to target… and that is important because 90% of drugs that go into clinical trials fail and most often that is because the wrong target is picked.
In terms of successes predicting the results of experiments — we can test ourselves by looking for “ground truths” about biology and chemistry – relationships and pathways that have been proven out in humans – that show up in our maps of biology and chemistry. When our teams search the map and see landmarks they expect, it gives them (and us) extra confidence to explore new ideas surfaced there.
And to your final question – while I can’t say exactly what we’ll charge for future medicines because we’re still fairly early in the development process, I do believe the best way to bring down drug prices is to industrialize the drug discovery process. If we can find a way to scale our pipeline, bringing better medicines to patients faster, with less failure, we can start to bend the cost curve. That’s our goal in the coming decades.

What are your "dream" partnerships? Are there any companies out there that you are excited/inspired by and would love to have by your side (Other than Bayer and Roche of course :))

I love this question. We’re really lucky to already be working with two dream partners! One with Bayer in fibrosis and one with Roche/Genentech in neuroscience and a single oncology indication.

What we look for in new, transformational partnerships are threefold:

1. Learning for us - can we learn from a partner to make the company better for the future?
2. Impact - can we drive value for patients and our shareholders?
3. Data - can we gain access to, retain access to, subsidize access to, or otherwise build our dataset?
   

[Edited - list formatting]

Are you limited by capital or by discovery? Eg have you discovered what you think are disease targets with unmet need where you’re reasonably confident you have a real target, but you have to deprioritize it due to trial costs? Or is the limiting factor finding targets and agonists/antagonists for them?

Neither. Time is the most limited resource. So much unmet need and so much science to explore. Having a searchable database of 3 trillion gene and compound relationships results in a superabundance of potential insights. We want to focus our efforts on those where we have the highest confidence in the compound<>gene relationship and that addressing this biology has a high likelihood of addressing patient needs. To do this, we integrate additional automated layers of information, such as transcriptomics and SAR tractability to accelerate discovery and reveal which insights have the highest potential to benefit our vision of a diverse pipeline of high-impact programs. We have to spend a lot of time onboarding folks to think this way and that’s why time is our most limited resource.

What do you see as the future of image-based profiling?

FTW

Would you rather fight 100 duck-sized horses or 1 horse-sized duck?

Clearly 1 horse-sized duck. Go for the achilles...

What are your ambitions/acticities around 3 dimensional cell assays/Co-cultivation/Organ on a chip technologies to further advance your phenomics studies and bring them closer to animal models and finally to humans?

We’ve done a lot of work on co-culture at Recursion and we agree that 3D assays have a lot of utility; as a company focused on innovation these are areas that are highly interesting to us. Unfortunately we aren’t able to discuss all the methods and areas of research but feel free to take a look at our [presentation from Download Day] for some flavor on where we are innovating (https://youtu.be/NcxccxI8PWQ).

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?

Q1 - We just open-sourced [RxRx3](https://www.rxrx.ai/rxrx3), the largest public dataset of its kind so far… but as for unblinding the rest… [insert picture of Dr. Evil with hairless cat]
Q2 - My biggest learning as a founder has been that the most complex thing in building a company with a mission as ambitious as ours is not the science, it is the people. Helping everyone here work at their maximum potential, together, and rowing in the same direction is and always will be (IMO at least), the hardest struggle.

Are you guys engaged in protecting your methods of drug discovery via patent applications? Or do you guys plan on protecting any potential candidates once their existence becomes known through the methods? Or both?

As a patent attorney, your model sounds interesting and I hope you protect your discoveries and inventions. I have been involved in patents relating to pharmaceutical design and drug development and have seen the various processes first hand. It definitely is an iterative and arduous process but it can be totally worth it in the end if you have that one successful candidate that proves therapeutically effective and obtains FDA approval.

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).

Heard that RXRX is 3x better than Moderna’s drug discovery yet moderna has way more drugs in the pipeline as well as many in phase 2 and 3. Isn’t mrna easier to work with vs small molecules? When do we see the 3x performance materialize?

Always great to hear from a fan… we’re blushing.

But your question is good - mRNA works really well in some important parts of biology - like tricking your body into thinking it has seen components of a virus so it mounts an immune response. But mRNA is not probably the right tool for other areas of biology (like inhibiting an overactive protein).

We think Moderna’s work is awesome

Do you plan to export/adapt your software internationally?
Do you plan to collect data from other laboratories (national and international research)?

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.

How do you balance time in dry lab machine learning predictions vs. experimental work in cells or animals to validate a compound?

We actually think about this a lot and we believe that these processes need to learn from each other. We build feedback and feed forward loops between dry lab and experimental work - essentially we think iteration is most important. We do up to 2.2 millions experiments in our wet lab each week to feed machine learning predictions and those predictions feed back into the wet lab experiment design. We do all of this in service of decoding biology and delivering therapeutics to patients.

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EDIT: Removed a typo.

When are you opening your first labs/offices in Europe (and where would you like them to be), so that you can also tap more extenisvely into the european talent pool without them having to relocate?

We don’t have any immediate plans for an expansion in Europe right now.

How is it looking in the field of curing hearing damage through auditory trauma along with hyperacusis?

We are not currently working on any auditory trauma indications, but are cheering on the organizations that are finding treatments.

Given the scale of opiate crisis and the general lack of reliable addiction treatment are you or your competitors looking into developing less or even non addictive pain management drugs?

Perhaps alternatives to opiates?

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.

Why do you suppose it has been so difficult for Recursion to keep a CSO (been without since 8/2021) and a CMO (been without since 6/2022)? How do you feel like the lack of such experienced leadership has affected your ability rapidly translate your insights into medicines?

I’m really hard to work for…
In all seriousness, almost all of the executives at Recursion today have been with the company for four or more years, and we are proud of that track-record. That said, we have a really ambitious mission at the intersection of many diverse fields, and we fully support our current leadership while we make sure we get the right people into these roles.

Do you see any use cases for looking at metagenomics data in your drug discovery or lead optimization efforts?

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.

1) The area of AI enabled Drug Discovery is a fast moving field: When have you planned to update the Frost & Suvillian Analys Slide showing the Top companies? It most likely will require regular updating.

2) What made you change the visualization of your pipeline slide? (Going from the Horizontal "scatter" Plot with the different programs from early discovery to clincal to the newer illustration of the bar plots, that is no longer showing the number of early stage programs)

We agree. It has been a while. Keeping up with all the great work in the space is hard, but this is on the list.

We changed the pipeline slide visualization based on feedback from lots of investors who appreciated seeing something they were more familiar with.

1 - On drug discovery: Are you researching new compounds, natural or synthetic? Or trying to map possible interactions of known compounds?

2 - Is your research in any way related to the work of AlphaFold?

OK, Imran answered this question, but he’s currently restarting his computer, because Murphy’s Law… so from Imran:
In our early years we focused on using our approach to enable drug repurposing programs (“known compounds”), hence why 4 of our 5 clinical stage programs are with repurposed molecules. But for the last few years we’ve been using our maps to discover & optimize novel chemical entities, including both natural and synthetic ones - in fact our first new chemical entity (synthetic compound) just entered Phase 1 clinical trials!

For 2, see above!

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?

We very much hope that the computationally-accelerated advancements in biology and chemistry one day results in exactly this - the ability to create the precise compound to treat a disease, even on the individual level. We think that may be a couple decades away, but we are going to keep pushing to make those crazy ideas real.

How can we cure muscular dystrophy!?

We are not working on this indication at this point in time as the genetics behind it are not a good fit for the technical parameters of our platform today, but it is a devastating disease and we are rooting for those who are actively pursuing discovery in that area.

Why have you stayed in SLC as your headquarters, vs moving it to Silicon Valley or Cambridge or somewhere more biotech dense?

There are pros and cons to any geography today, many of which are being blurred by the move to (or from) remote work. We ended up in Salt Lake City serendipitously. I spun the company out of my dissertation work at the University of Utah with my co-founders back in 2013.

As we grew the company, we found a lot of great scientific and technical talent here in Utah. However, we had a harder time finding experienced, senior talent from biotech and pharma in the area. What that meant is that we had to build a really strong recruiting arm to the company, but once people commit to Recursion they tend to stay for a long time with little turnover, which is huge for us when building something this complex. We’re a proud leader of Utah’s Biohive community and believe deeply in the community we’ve created here in SLC. Not to mention all the fun things that come with being based in a mountainous state!

That said, we are now ~500 people and want to have the best talent in the world, and so we have remote staff, as well as teams in CA and Canada. And we certainly could imagine opening offices in other places in the future.

We all know that chat GPT made mistakes at its beginning - which is the point of machine learning and IA. But considering that your IA is in the pharmacetical domain, this is more of a life or death situation. How do you plan on dealing with such mistakes?

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.

What the most interesting drug you've discovered so far in terms of use?

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

Doesn't the use of AI massively restrict the creative discoveries that could be made, restricting the discoveries to the information base present in the AI's machine learning algorithms?

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.

What are the similarities between your AI and ChatGPT ?

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!