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

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 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!

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.

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.

Thank you-- this is an interesting perspective! I spend large amounts of time convincing structure-based scientists that dynamics, thermodynamics, and kinetics are important to understand drug binding and biological function (and especially allostery), so circumventing structure seems like a whole other paradigm.

If you can point me to any comprehensive papers describing your approach, I'd be really grateful!

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

That's infinitely more fun, thank you for the reply!

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.

Yes! Take a look at my related reply here.

GNNs are in the suite of methods that we use and evaluate. But it’s useful to recognize that although we often draw molecules as graphs, that is not necessarily the only useful (or best) representation for molecules in machine learning. We recently published (poster and talk, paper) research using DeBERTa-style representations and self-supervision over molecular graphs, achieving SOTA results on 9/22 tasks in the Therapeutic Data Commons ADMET tasks.

We actually don’t start our drug discovery efforts from single targets – check out my earlier reply in the AMA for more details. ChEMBL certainly is an excellent source of structural information, but our insights come not from these data, but rather from high-dimensional relationships between cells treated with compounds and genetic knockout. We advance series of compounds using this data prior to having any information about the target itself.

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.

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).

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.

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.

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

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.

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?

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

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

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.

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.

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!

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