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?

Ha!

Nonsense. $100 for a backpack that isn't for some specialized purpose is ridiculous.

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.

For your repurposing efforts, have you considered partnering with one of the large-scale EHR data providers and running causal inference algorithms to try to identify potential unexpected effects of certain therapeutics or combinations thereof in longitudinal outcome data?

FTW

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. How is the job market for biotech 2023/2024? Especially for computational scientists?
2. I’m a Comp Chem PhD graduating end of 2023, looking to switch to CADD. What qualities are you guys looking for from a computational drug discovery scientist apart from those mentioned in the job descriptions? Thank you!

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?

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!

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?

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 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.

Did your child outgrow our Tinypak?

Fear not! Allow me to introduce the Mediumpak!

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]

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

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?

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?

Do you have the capability to utilize available ultra large chemical spaces?

At some point, will you be able to connect such implicit, non-enumerated spaces with predicted activity?

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?

To what extend (if any) do you think that a database profiling common human genetic variation in eg KRAS tumors would be helpful so that you can design antibodies that will be broadly applicable? Do you analyze mass datasets from eg TCGA or Genomics England and try to design antibodies considering common variants or do you pick a canonical target and work from there?

How does your technology compare to this automated scientist platform? https://www.biorxiv.org/content/10.1101/2023.01.03.521657v1

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?

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?