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.

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

Viagra was studied for use against high blood pressure before it became a boner pill.

So, data sharing in industrial science is complicated. I’ve spent my career in biotech driving for greater openness and data release in the companies where I’ve been. The “natural” state of data is to be siloed. This isn’t just an industrial thing – I’ve read plenty of papers from academic groups with “data available on request” (lol nope, I tried) – and the driver is always the same: a fear that “we spent this money to make the data, how do we get value out of it?”

One of the reasons I joined Recursion in 2019 was that Chris and the team shared that commitment to sharing learnings back to the world. The balance we’ve struck to support open science, but also use this data to drive internal research and develop therapeutics as a public company, is to share a huge dataset that is partially blinded. In RxRx3 we are revealing ~700 genes and 1600 compounds. We’ve sometimes chosen different points on the balance; for example, our COVID datasets RxRx19a and RxRx19b were released completely openly (CC-BY) because we thought the public health crisis was more important than any commercial interest we might have in the data. Our current aim is to continue to unblind parts of the RxRx3 dataset over time, so please stay tuned for additional releases over time.

We have also contributed to open science releasing not just datasets, but tools. Associated with our COVID datasets, we released a data explorer allowing folks to explore the results from our COVID screens. Along with RxRx3, we released a tool (MolRec) where people outside of Recursion can explore some of the same insights that our scientists use to generate novel therapeutic hypotheses and advance new discovery programs, and get a look at how Recursion is turning drug discovery from a trial-and-error process into a search problem.

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.

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I'll take the first recursion joke I can I'll take the first recursion joke I can I'll take the first recursion joke I can ...

hahahhahaba I lold

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

​

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

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.

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]

Might be some personal bias here – I come from a sequencing background before Recursion – but I don’t necessarily think metabolomics or proteomics are more established than transcriptomics (especially in a research context; clinical testing is different!). The past 10-15 years have seen an absolute _explosion_ in the ability to generate (and analyze/interpret) sequencing data at scale. One of our core principles is being able to generate high-dimensional data at scale, and from that perspective, transcriptomics is a great complement to phenomics. Metabolomic and proteomic technologies (whether affinity or MS-based) are still more expensive and smaller scale than what you can achieve by sequencing. That being said, as technology advances and we find the right application areas, we’re interested in exploring what these other readouts can do for us.

1 - We aim to close the loop between high-dimensional, biological profiling of compounds and rapidly learning how to drive the compound series’ evolution to higher potency, lower risk and better kinetics. This is a huge and critical component of the overall vision of industrializing drug discovery. In practice we are dedicating major efforts into ML-guided SAR and how automated synthesis integrates into this plan is part of our roadmap.

2,3 - given the highly custom nature of the automation systems we have built, and the need for ultra-high control over experimental precision, we have relationships with several automation experts in this space. As far as partnerships in this space are concerned, we can’t comment on specific business development plans or transactions until we announce them publicly. What I can say is that we recognize the work it has taken over the last decade to map and navigate biology, and we believe there are many other teams and technologies that have been developing in parallel and we’re always exploring options to bring in additional capabilities that may accelerate our mission.

4 - The “Recursion 101” video we released in October of 2022 has some of the most current footage of our automation labs — if you haven’t seen the video, we (selfishly) think it’s worth the watch. We have also released “Recursion's Mapping & Navigating Demonstration” which shows footage of our laboratories.

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.

I’ve been super excited to see how our datasets have driven academic research out in the world. Recursion has been on the cutting edge of developing phenomics as a high-throughput biological modality, and the RxRx datasets are among the largest and best-organized public datasets out there for folks to work with. I’ve seen blog posts, conference posters, MS theses, and more written on our datasets. (We’ve also hired a number of folks to our team based on their work on these data!)

I mean, have you seen that guy? What possible evidence could you conceive of that would make it NOT Mason Victors... I contend there is no such evidence.

FTW

Batch effects are probably the most annoying part about doing machine learning in biology – if you’re not careful, ML methods will preferentially learn batch signal rather than the “real” biological signal you want.

We actually put out a dataset, RxRx1, back in 2019, to address this question. You can check this here.Here is some of what we learned (ourselves, and via the crowdsourced answers we got on Kaggle).

Handling batch effects takes a combination of physical and computational processes. To answer at a high level:

1. We’ve carefully engineered and automated our lab to minimize experimental variability (you’d be surprised how clearly the pipetting patterns of different scientists can come out in the data – which is why we automate).
2. We’ve scaled our lab, so that we can afford to ($ and time!) collect multiple replicates of each data point. This can be at multiple levels of replication – exactly the same system, different batches of cells, different CRISPR guides targeting the same gene, etc. – which enables us to characterize different sources of variation. Our phenomics platform can do up to 2.2 million experiments per week!
3. We’ve both applied known computational methods and built custom ML methods to control / exclude batch variability. Papers currently under review!

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.

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).

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.

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

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

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.

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.

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!

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

On a scale from Darkwing to the duckling in my kid's bedtime book that wandered away from his nest after specifically being told not to, what kind of ducks are we talking about?

finally a question i understand.

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.

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.

Yes - our digital chemistry platform allows our scientists to search and expand hits across multi-billion molecule virtual libraries and growing!

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.

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.

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.

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).

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.

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

I don't work at recursion but I'd venture the answer is that they have a variety of libraries ranging from oligos, small molecules, to crispr constructs.

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.

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.

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

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!

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.

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

Yes! Take a look at my related reply here.

I did my PhD in the Folding@home lab, so I like this one. There’s a distinction between what’s formally called “ground-state structure” and “structural dynamics”. “Ground state structure” is the lowest-energy, most stable structure of a protein; for me, the ground state structure is “lying in bed”. But only knowing that doesn’t tell you how the structure moves around, which it turns out is important. For example, when I sprained my shoulder, the movement of my arm was highly restricted, but you wouldn’t have known that from looking at one position in which I sleep (you creep). Folding@home is more focused on modeling the dynamics of proteins than their ground state structures. For example, the most effective recent COVID vaccines used a modification to the spike protein called “S-2P”/”prefusion-stabilized” that effectively froze the protein in one particular shape rather than allowing it to fluctuate, which enhanced its ability to generate a useful immune response.
That said, dynamics is the obvious next step for ML methods in protein structure, so I would not be surprised to see new developments here!

We build maps of biology in a range of cell types for exactly this reason – different cell types express different genes. For example, in our partnership with Roche and Genentech, we are building maps in a range of neuroscience-relevant cell types to capture their unique biology.

The majority of drugs don’t fail because we can’t engage the target with a small or large molecule - they fail because we pick the wrong target. Hence our focus on mapping and navigating causal biology. Our platform is exceptionally well-suited to target-agnostic identification of compounds that impact biology, which absolutely means we don’t always know the target of our compounds. However, one of the major advantages of our map is that it can often uncover the real targets of our active compounds, enabling us to use advancements in structure-based. Additionally, the underlying learnings in this field are even useful in the target-agnostic space, as we try to featurize compounds and learn how to make molecules not only more potent against their primary target, but also in enhancing their overall efficacy, safety and metabolic profile.
That said, we actually do make use of structure-based methods where appropriate. What we don’t do is limit ourselves to solely identifying particular targets (and their structures) ahead of time when initiating discovery programs.

I can’t comment about all of our internal technologies. But! We did recently publish work with our collaborators at Genentech on benchmarking methods to builds maps of biology, which we evaluated on both our phenomics data and (publicly-available) 10x scRNA-seq (Perturb-seq) data – check it out here. So, draw your own conclusions…

We run our experiments in house so that we can control the quality and relevance of the data. This type of attention to detail requires doing a lot of the unsexy behind-the-scenes operational improvements to control for as many 'exogenous' factors that can influence what actually takes place in our experimental wells. To manage this, we have (to an extent) backward integrated with our supply chain so that we can (i) anticipate where possible or (ii) correct for changes in the media our vendors supply, different coatings that suppliers may put on plates, etc... Additionally, we have built an incredibly robust tracking process that allows us to measure the meta data from every step in our multi-day assay, so that we maintain precise control over things like volume transfers, compound dwell times, plate movements, etc. to further ensure this relatability. I also wrote more earlier in the AMA about how we handle batch effects!

https://imgflip.com/i/7ac8l6

Conformal prediction is indeed an interesting method (or family thereof). I can’t comment on our undisclosed internal machine learning research, but what I can say is that machine learning on biological problems tends to be much, much harder than that on common toy or benchmarking datasets. Uncertainty quantification is usually an even harder problem than pure accuracy measurement, especially when you have a mix of known and unknown systematic and random effects in your data-generating process.

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.

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.