ShakeNBakeGibson OP t1_j7qcsog wrote

Please see the following paper with many helpful refs ( 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.


ShakeNBakeGibson OP t1_j7n216a wrote

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.


ShakeNBakeGibson OP t1_j7n0xuf wrote

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]( 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.


ShakeNBakeGibson OP t1_j7mzmiu wrote

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.


ShakeNBakeGibson OP t1_j7mze4o wrote

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 (


ShakeNBakeGibson OP t1_j7myf9q wrote

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.


ShakeNBakeGibson OP t1_j7mwav6 wrote

Q1 - We just open-sourced [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.


ShakeNBakeGibson OP t1_j7mttku wrote

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.


ShakeNBakeGibson OP t1_j7mteeo wrote

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.


ShakeNBakeGibson OP t1_j7msdaz wrote

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]( and [publications](


ShakeNBakeGibson OP t1_j7mqi3l wrote

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]( 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.


ShakeNBakeGibson OP t1_j7mpbro wrote

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.


ShakeNBakeGibson OP t1_j7molmg wrote

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!


ShakeNBakeGibson OP t1_j7mncyd wrote

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.


ShakeNBakeGibson OP t1_j7mmu8i wrote

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


ShakeNBakeGibson OP t1_j7mka71 wrote

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.


EDIT: Removed a typo.


ShakeNBakeGibson OP t1_j7mj1gd wrote

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.


ShakeNBakeGibson OP t1_j7mh7nq wrote

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.


ShakeNBakeGibson OP t1_j7mfids wrote

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!