Biology is enormously complex and highly networked.

As a species, we so far only understand a tiny fraction of all there is to learn on the subject, despite millions of incredible scientists having dedicated their lives to uncovering its truths. Unraveling the complexity of biology is a path to better medicines.

The emergence of radical technological innovations has created the opportunity to envision new approaches to discovering therapeutics more efficiently and at scale. We are pioneering the integration of these technological innovations across biology, chemistry, automation, data science, and engineering to modernize drug discovery and development.

There are a lot of things that make Clēnera possible. Our breakthrough technology. Our network of partners. The leading talent of our team. But the thing that makes us great is how all of those things work together—and the way those relationships make new things possible for you.

Our integrated Recursion Operating System creates a closed-loop system combining proprietary in-house data generation and advanced computational tools to generate novel insights to initiate or accelerate therapeutic programs. We iterate on this approach to create a virtuous cycle of learning within our system and progress programs at each stage of discovery and pre-clinical development.

Integrated wet lab & dry lab

In silico predictions are validated in our own wet laboratories, and repeated, creating a mutually reinforcing cycle of learning. Predictions that validate experimentally are advanced rapidly and reinforce our learning. Predictions that do not validate experimentally generate valuable data that test our understanding and can be used to retrain or reweight the algorithms to improve future predictions. This iterative process of prediction and validation is a key element of successful machine learning over complex datasets.

Unconstrained by human bias

Human bias is often a major threat to the drug discovery process. As humans, we are limited in the size and scale of data we can interpret and are prone to seeing the data that suits us and justifies our hypothesis. Our machine learning tools are designed to extract insights from foundational biological datasets that are too complex for human interpretation, minimizing human bias and identifying relationships that traditional drug discovery approaches may miss.

Scale, scale, and scale again

Since 2017, we have approximately doubled the capacity of our phenomics platform each year and currently generate up to nine million images or 80 terabytes of new data to the Recursion Data Universe per week across 1.5 million experiments.

Reshaping the drug discovery funnel

Our goal is to leverage technology to reshape the typical drug discovery funnel by:

1.

Broadening the funnel of potential therapeutic starting points beyond hypothesized and human-biased targets

2.

Rapidly narrowing the funnel by identifying failures earlier in the research cycle when they are relatively inexpensive

3.

Accelerating the development of high potential drug candidates

We are a biotechnology company scaling more like a technology company, and we are just getting started.