New Methods For Drug Discovery
Discovering new drugs has become increasingly expensive and complex in the last few decades, with new therapies costing more than a billion dollars to be developed.
This is partly because all the low-hanging fruits are already picked, like known medicinal plants and “easy” to find biochemicals.
Another factor is that the diseases yet to find an efficient treatment for are the most complex ones, often caused by a body complex dysfunction (diabetes, Alzheimer’s, cancer, obesity, etc.) or hard-to-reach causes (for example parasites and viruses like HIV or malaria, very good at evading the immune system).
This means that for discovering new molecules now, hundreds of thousands or even millions of compounds must be considered before narrowing it down to a few.
This is a daunting task, and an expensive process as well.
Luckily, progress in AI and computation has made it possible to comb through astronomical volume of data, at a much lower cost in both dollars and time.
And two companies are now merging to speed up the adoption of AI-driven drug discovery.
Exscientia & Recursion Merger
On August 8th, 2024 the merger of Exscientia with its larger peer Recursion Pharmaceuticals was announced.
Both companies had developed their own process to leverage AI and automation to speed up drug discovery, as well as reduce costs.
A strong argument for the merger is that both companies’ technologies are quite complementary. We will look in detail below at both companies, but the overall picture is such:
- Recursion is focused on “first-in-disease” opportunities, through a deeper understanding of biological mechanisms.
- Exscientia is focused on “best-in-class” drugs, with expertise in precision chemistry and molecular synthesis.
So together, the combined company not only gets some additional scale to save money on overhead costs and regulatory compliance (usually the goal of mergers in biotech) but also the combination of excellence in chemistry synthesis AND biological insights.
Exscientia
The company is using AI to develop precision therapies.
It runs a “full stack” AI drug discovery technology with dedicated software at every stage of the drug discovery process.
So instead of looking at existing molecules, Exscientia’s Precision Design AI designs custom molecules to match the target found by its Precision Target AI.
Source: Exscientia
Exscientia’s technology reduces 70% of the time required for going from a biological target to finding a corresponding drug and an 80% more capital-efficient process.
Part of the time and cost saving comes from a highly automatized process, with “comprehensive robotic automation across the entire experimentation cycle”.
This resulted in 4 compounds in early clinical stages, 30 programs in total, and $6.5B in revenues from milestones with partners. The main focus has been oncology (cancer) and inflammatory diseases.
Source: Exscientia
Recursion Pharma
Recursion Pharmaceuticals leverages AI in drug discovery. The more AIs get involved in drug discovery and development, the more data will become precious for training the AIs.
Biology is an extremely complex field, with integrated and verified data sometimes in short supply. This is a serious problem when any error will create bias, limitations, and errors in the AI, which might then need to be retrained from scratch.
So creating solid datasets has been the focus of the company since its inception looking to solve several problems with biodata:
- Analog data, from faxes to pdf or scanned printouts.
- Siloed data, with little to no annotations.
- Hard to replicate research.
To solve these problems, Recursion created one of the world’s largest automated wet labs, and digitized millions of their own experiments (2.2 million experiments per week).
It combines dry lab (in-silico) and wet lab (biological samples) with:
- A library of 1.7 million small molecules.
- Cell cultures, CRISPR gene editing, soluble factors, live viruses, etc.
- An automated laboratory robotics workflow that allows for up to 2.2 million experiments each week.
- High-throughput microscopes and sequencing systems.
- Continuous video feeds from cameras, recording holistic measurements of animal behaviors.
- Advanced computational resources, which have generated >21 petabytes of proprietary high-dimensional data.
- ADMET (absorption, distribution, metabolism, excretion, and toxicology) data.
This creates unique (and massive) datasets at all levels of the “multiomics” biosciences, including proteomics (protein levels), transcriptomics (mRNA levels), phenomics (cellular morphology), ADMET and “in-vivonomics” (animal behaviors). The company is also looking to add metabolomics and genomics to its datasets in the future.
Source: Recursion
(you can read more about why multiomics matters in “Multiomics Are The Next Step In Biotechnology”).
So while Exscientia started by studying biological mechanisms and designing a drug for it, Recursion instead built from scratch a massive database of standardized and replicated biological research.
Recursion also acquired in May 2023 the drug chemistry-focused preclinical startups, Cyclica and Valance, for a total of $87.5M.
They also own one of the world’s fastest supercomputers to train their LLMs and AIs for drug discovery. Models were trained on a library of more than 2 billion images and inferred 6 trillion relationships between all possible combinations of genes and compounds.
Source: Recursion
Recursion established a partnership with AI leader Nvidia and might release some of its AI models to commercial partners via NVIDIA’s new BioNeMo platform. It will also give Recursion priority access to NVIDIA’s latest GPUs through NVIDIA DGX™ Cloud.
The company has also received a $50M investment by NVIDIA in July 2023.
Recursion’s R&D proprietary pipeline is mostly focused on rare diseases and oncology, with 3 candidate drugs in phase 2 of clinical trials.
Source: Recursion
For more complex sectors, like neuroscience, and undruggable oncology, the company prefers to establish partnerships with established companies in these sectors. For example Roche in neuroscience and Bayer in undruggable oncology targets and fibrosis.
In total, the company is looking at $13B in potential milestones across 50+ possible programs plus royalties.
Lastly, the company has established relations to license out its technology and data, especially when data exchange can be negotiated to boost the information both companies can use in the future.
Post-Merger Picture
Ownership & Management
Overall, while technically a merger, it seems to be somewhat of an acquisition of Exscientia by Recursion.
The merger is organized so that Exscientia shareholders will receive 0.7729 of Recursion Class A common shares. Post-merger, the Recursion shareholders will own 74% of the combined company and Exscientia shareholders 26%.
The transaction should be closed by early 2025.
Recursion’s CEO and co-founder will be the CEO of the combined entity, while Exscientia’s CEO will become the Chief Scientific Officer.
The company location might however become a little bit complex, with >850 employees spread among the many inherited sites in Salt Lake City, London, Toronto, Montreal, San Francisco Bay Area, Oxford, Boston, Vienna, Dundee, and Miami.
Most likely, some relocations and mergers of facilities might be in order in the medium term.
Merged R&D
The merged R&D pipeline is equally split between oncology and rare diseases, with 4 programs in phase 2 of clinical trials.
Source: Recursion
For the future of research at the company, many of the research steps can be supplemented by the merger of recursion and Exscientia data and AI tools, especially in the initial stages.
For example, Exscientia’s automated chemistry synthesis mix is a strong addition to Recursion’s world largest automated wet lab.
The deep understanding of biology and disease of Recursion can also be used to better pick what is useful in the millions of physical compounds and billions of target predictions of Exscientia.
Source: Recursion
In addition to this proprietary pipeline, the company will also have 4 large strategic collaborations (e.g., Roche, Bayer, Sanofi, Merck KGaA) with 10 programs already optioned across oncology and immunology.
It is likely that the wider range of ongoing collaboration will also give the company’s management some leverage in future strategic collaboration discussions, as they will now have additional options to put the large pharmaceutical companies in competition with each other.
Source: Recursion
Financial Data
Both companies are mostly pre-revenues, with no drug on the market yet. The existing cash comes from successive fundraising and income mostly from reaching milestones in research programs established with larger pharmaceutical companies.
The combined business will have a $850M treasure chest of cash, giving a runway until 2027.
The next 2 years should see up to $200M in potential milestone payments for R&D successes. Most of the research programs are targeting large markets, with the majority of them aiming for >$1B in sales if successful.
The Future Of Drug Discovery
It is a growing trend that AI and big data will be key in finding new cures and better drugs in the future.
It is also becoming very clear from LLMs and other AI initiatives that data is king in this sector.
So we should be expecting mergers like the one of Exscientia & Recursion to keep happening in the future. We discuss many of these in our articles “Top 10 Biotech Big Data Companies” and “Top 5 AI & Digital Biotech Companies”.
Among the other dataset that could be useful in the future to Recursion, or other similar AI-drug discovery companies are:
- Spatial genomics, or the study of the genome and transcriptome in 3D, allows visualization of the activity of genes at the cellular or even intracellular level.
- Physics-based molecular modeling.
- Schrödinger (SDGR) is a leader in finding the best possible molecule for a given goal, balancing out conflicting metrics like potency, solubility, half-life, synthesizability, etc.
- DNA reading and sequencing
- Synthetic biology, or the engineering on-demand of new traits or organisms for specific purposes, whether medical, biotech, or industrial.
