The $RXRX Thesis
Originally published: 12/29/2024. Last revised: 12/29/2024.
Disclaimer
This document is for informational purposes only and does not constitute investment advice. I am not a licensed financial advisor, and the opinions and information presented here are based on my personal knowledge and research at the time of writing. While every effort has been made to ensure accuracy, some information may be incorrect or subject to change. Readers should independently verify all data and consult a qualified financial professional before making any investment decisions.
Preface
Welcome! I’m about to throw a wealth of information and figures your way regarding one of our favorite stocks among the AfterHour crowd, $RXRX, a.k.a. Recursion Pharmaceuticals (I’ll refer to them by the ticker throughout this post as to ease some length). I’ll try to divide this post into sections to make it “easily” digestible (you’ll be able to skip around, should you wish.) My education regarding RXRX stems from many hours of researching and cross-referencing through various different sources, such as Recursion’s website, it's 270 page narrative released earlier this year titled "Recursion Decoded", a story on the beginnings of RXRX and their evolution throughout the years, filings, financial info & figures gathered from Trading View, NASDAQ, and Yahoo News verified publications, among other more minor but verifiable sources. All notes taken on the subject were hand-written for the sake of my own understanding and to present genuine authenticity as this was, for the most part, a “No GPT” project of mine (except to help me understand some high-level scientific topics that is just, quite frankly, outside the realm of my own understanding, as I am not a computer scientist, AI expert, or medical expert holding several degrees related to computers, chemistry, biology, or related majors, as well as creation of visuals, where I manually plugged in the data found through official sources and verified visuals for accuracy.) Some of this info will be “copy-paste esque” (similar wording available on their website but more or less information on here due to cross-referencing other sources or absurdly long PDF’s on their website with too many details to list) due to the sheer amount OR lack of information available. If you have any questions or this work needs to be corrected due to incorrect information (which I’m highly confident that all should be correct), please feel free to leave a comment or shoot me a message on discord and I’ll be sure to revise or address the issue brought up. Should you wish for me to expand on a topic, please let me know as well and I can certainly come back in here and add additional info or amend current info. All information is dated as of 29 Dec, 2024 or prior, including figures. With this being said, let's get right into it.
I - Background and Leadership
I.1 - Company Background
RXRX was founded on November 5, 2013 and began trading on NASDAQ through an IPO on April 16, 2021. The mission of RXRX is the goal of understanding and decoding biology through advanced technology, such as automation and artificial intelligence, to radically improve lives. Starting modestly with only 6 employees, a small office, and a utility room converted by staff to be used as a lab, they have grown exponentially in all areas to its current state today. By 2018, RXRX had relocated its facility to its current location, with a staff 45 strong, to Salt Lake City and began its first FDA-approved clinical trial. Within a single year, staffing had more than doubled to 100+ employees and had grown to 150 in 2020. Its rapid expansion was made possible through several successful series of private funding throughout the years. During the pandemic years, RXRX capitalized on the state of Covid-19, as it worked to fight against the disease, partnering with several other companies who utilized its newly released open source dataset released in April of 2019. In 2021, the state of RXRX was forever changed as the Recursion Supercomputer was officially activated and named the 85th fastest supercomputer on the planet, showcasing a major milestone for the company. Today, RXRX employs over 500 employees and continues to lead innovation in the TechBio sector, driving advancements in biology and technology never-before-seen, reimagining how medicines are made.
I.2 - Chris Gibson, Ph.D.
Chris Gibson is one of three co-founders and current CEO of RXRX. Gibson hails originally from Rice University, where he majored and obtained degrees in bioengineering and management. After graduation, Gibson began working with Dr. Dean Li in a lab as part of his Ph.D studies, at the University of Utah, where the early stages of RXRX began to take shape. After completion of his Ph.D and time spent in the lab with Dr. Li, Gibson thrust himself into RXRX and started building it up from the ground up. Today, Gibson also serves on the board of BioHive, a public-private partnership helpting to drive exampsion of Utah's life-science ecosystem.
I.3 - Dean Li, Ph.D.
Dr. Dean Li is the second of three co-founders and current serving board member of RXRX. Dr. Li found his start in the biotech industry at the University of Utah, where he was involved with cutting-edge translational medical research for over two decades. While researching, Dr. Li helped found several biotech companies that were made possible through his research, such as RXRX, Hydra Biosciences, and Navigen Pharmaceuticals. Today, he is the President at Merck Research Laboratories and has been working in managerial roles at Merck since 2017. He remains very involved with the University of Utah as well, having served many positions in the past related to the medical and research centers, with his biggest achievement of responsibility at the University being serving as the Chief Scientific Officer of University of Utah Health Science Center.
I.4 - Blake Borgenson, Ph.D.
Blake Borgenson is the last of three co-founders and current serving board member of RXRX. Similar to Gibson, his background is also from Rice University, where he obtained a bachelor's degree in electrical engineering. Following Borgenson's graduation, he spent time in Switzerland researching and building medical software for surgical protocals, co-founded a very successful e-commerce company that currently employs over 350 personnel, and completed his Ph.D. in bioinformatics at UT Austin, where he began utilizing machine learning to exploit new experimental techniques in rapidly mapping protein complexes.
I.5 - Other notable executive leadership
Other notable executive leadership include:
Ben Mabey, Chief Technology Officer, who oversees the technological infrastructure, often involved with machine learning, automation, and data science.
David Hallett, Chief Scientific Officer, who is hailed by RXRX as an "experienced drug hunter" with 20 years of experience leading successful scientific teams and driving strategic collaboration. Hallett is also the prior Chief Scientific Officer and Interim CEO of Exscientia, a company that RXRX aquired earlier in 2024.
Najat Khan, Chief R&D Officer and Chief Commercial Officer, who has deep expertise in the pharmaceutical and healthcare industry sectors involving biological, chemical, and medical science as well as computational and data science. As the prior Chief Data Science Officer and Global Head of Strategy and Portfolio Organization at Johnson & Johnson, Khan plays an integral role in helping to build an industry-leading pipeline to help deliver medicines through the use of data science and AI at scale.
It is also important to note that executive leadership is currently in the process of ongoing changes, due to the aquisition of Exscientia by RXRX, among other factors.
I.6 - General Employee Background
The now 500+ employees working for RXRX is comprised of life scientists and computational and technical experts, among various other legal, scientific, and computer-saavy employees. The idea behind RXRX's employee structure and hiring is to help create an environment where empirical data, statistical rigor, and creative thinking is brought to the forefront to help address problems that RXRX deals with on a daily basis, both on a small and large scale.
II - Major Partners
II.1 - Types of pertners
RXRX, with the goal of always fostering partnerships to create therapeutics, takes multiple approaches to form partnerships in 3 distinct ways:
Strategic Collaborations: Typically identifies as partnerships with leading biotech companies, pharmaceutical companies and academic research institutions. Typically involved in identifying novel therapeutics and unlocking biological insight.
Asset Development Partnership: Typically involves in-licensing rights to specific assets provided by other companies to advance internal programs.
Discovery Platform Partnership: Typically involves collaborating with third parties to explore diverse disease domains and leveraging partner's deep domain expertise in conjunction with RXRX's capabilities to map and navigate complex biology to identify and rapdily advance potential therapeutics.
II.2 - Therapeutic Partners
Roche & Genentech - Established Dec 2021 as a multi-year collaboration in key areas of neuroscience and an oncology indication.
Up to 40 programs spanning over a decade or longer
Bayer Pharmaceuticals - Established Nov 2023 as a multi-year collaboration for a select set of precision oncology programs.
Up to 7 oncology programs with the potential of success-based future payments of up to $1.5 billion plus royalties on net sales.
Marck KGaA - Established Sept 2023 as a multi-year collaboration that leverages their expertise in oncology and immunology & clinical development capabilities.
Sanofi - Established Jan 2022 as a multi-year collaboration to develop an AI-driven pipeline of precision-engineering, small molucle medicines.
Upfront cash payment of $100 million, with potential of up to $5.2 billion in total aggregate milestones payments plus tiered royalties.
Bill & Melinda Gates Foundation - Established Sept 2021 as a multi-year collaboration to develop small molecule therapeutics to help prepare for the fight against future pandemics.
$35 million equity investment into Exscientia, a company now absorbed into RXRX.
Rallybio - Established 2019 as a co-development and co-ownership joint venture to investigate treatments for patients with rare diseases, targeting ENPP1 in patients with hypophosphatasia (HPP).
II.3 - Technology, Data, and Capability Partners
NVIDIA - Established July 2023 as a multi-year collaboration to accelerate the development of AI foundation models for biology and chemistry utilizing the NVIDIA DGX Cloud.
In Nov 2023, committed to working with NVIDIA to expand BioHive-1, RXRX's on premise supercomputer, into its next iteration, BioHive-2, to increase computational capacity by 4x+. Estimated to be in the top 50 most powerful supercomputers in the world in any industry and most powerful supercomputer owned and operated by any biopharma company upon completion.
NVIDIA currently holds, in its own stock portfolio, 7,706,363 shares of RXRX. At the time of writing this, the stock is currently worth $7.50 per share, making this investment worth $57,797,722.50.
Tempus - Established Nov 2023 as a multi-year collaboration to gain preferred access to one of the world's largest proprietary de-identified, patient-centric oncology datasets to support discovery of potential biomarker-enriched therapeutics at scale through the training of causal AI models.
Helix - Established May 2024 as a multi-year agreement to access hundreds of thousands of de-identified records of genomic data and data from longitudinal health records for use to train causal AI models and design biomarker and patient stratification strategies across broad disease areas.
Enamine - Established Dec 2023 as a multi-year collaboration to generate and design enriched compound libraries for the global drug discovery industry by leveraging RXRX's MatchMaker tool to identify compounds in the Enamine REAL Space to generate more powerful compound libraries for drug discovery purposes.
Google Cloud - Established October 2024 as an expanded multi-year collaboration to leverage Google Cloud's technologies to support RXRX's drug discovery platform. Includes exploring generative AI capabilities, including Gemini models, to support RecursionOS, drive improved search and access with BigQuery, and help scale compute resources. RXRX will also explore making some of its own AI models available on Google Cloud.
III - Major Product Offerings
As described perfectly by RXRX themselves, RXRX is the result of a combination of Eroom's Law, which states that drug discovery is becoming slower and more expensive over time (as the biopharm industry faces pressure amidst the declining efficiency in drug discovery), and Moore's Law, which states that the number of transistors on an integrated circuit will double every two years with minimal rise in cost (computing power is becoming faster and less expensive over time). RXRX leverages this radical combination of laws as it drives a new revolution in the pharmaceutical industry.
III.1 - Recursion Operating System (RecursionOS)
Recursion OS is a platform powered by one of the world's largest proprietary biological and chemical datasets. It's main mission is to help build maps of biology and chemistry that broaden the search beyond just a handful of diseases and existing therapeutic hypotheses to allow RXRX to explore unknown areas of disease biology to enable faster and better drug generation, running millions of experiments every week.
Recursion OS turns physical atoms of diseases and other info into digital bits to be used by RXRX in their deep learning models as digital representations in an iterative loop of experimentation and prediction. This creates relationships between all possible logged combinations to help faster facilitate research and discovery of new and effective drugs. To date, RXRX has generated 4 trillion searchable relationships between materials, down to single atoms, in their dataset. More relationships are still being added every day, as new relationships are formed and adds onto existing data to create improved predictions.
At the core of Recursion OS lies their in-house data generation in wet laboratories, where they conduct millions of experiments across every human gene and their library of chemical compounds to generatre multi-layered datasets readily available for mapping. This data spans approximately 60 petabytes - in other words 60,000,000 GB - one of the world's largest biological and chemical datasets. This dataset, while already massive, is also designed to expand over time.
RXRX follows a 3 principle strategy to data generation:
Scalability: "No static dataset will ever be sufficient to decode the vast space of biology. Our dataset is designed to expand over time as we test and validate predictions experimentally."
Reliability: "Reliable and accurate data is essential to reproducibility. We use highly controlled and standardized protocols while correcting for any variability in the technical execution of experiments to generate quality data."
Relatability: "We build connected datasets, enabling comparisons across time and experimental methods. That way, the data we generate tomorrow can be related to data generated five years ago."
Recursion OS also allows a multi-pronged approach to a capital-efficient business strategy:
Pipeline Strategy: Building internal pipelines with indications of potential for accelerated path to approval, with a current focus on precision oncology and rare diseases.
Partnership Strategy: Leveraging partner knowledge, clinical development, and financial backing capabilities in complex therapeutic areas, with a focus on neuroscience and other large, intractable areas of biology.
Data Strategy: Directing generations of data to maximize pipeline and partnership value-drivers by licensing subsets of data and key tools.
III.2 - LOWE Limited Language Model (LLM)
L - Large Learning Model
O - Orchestrated
W - Workflow
E - Engine
LOWE is a new LLM, similar to ChatGPT, that helps orchestrate complicated workflows involved with chemistry and biology using natural language to interact with biological and chemical data. LOWE is built off of date provided by and integrated into Recursion OS, and is the next natural evolution of Recursion OS. Using state-of-the-art artificial intelligence, LOWE puts tools easily accessible and readable into the hands of every scientist at RXRX in a simple and scalable way.
LOWE is able to perform critical, multi-step tasks in drug discovery, such as identifying new therapeutic targets, designing novel compounds and libraries, and predicting ADMET properties (ADMET stands for Absorption, Distribution, Metabolism, Excretion, and Toxicity; This is a factor heavily involved in drug discovery and development.)
LOWE also helps streamline collaboration between teams in Realtime, no matter medical or technological teams, making communication of research and studies easily accessible through a user-friendly interface utilizing the natural human language. LOWE is also able to create visuals related to retained data to help facilitate further understanding of topics searched by its end user.
In short, LOWE reduces the need for manual inputs and complex coding that is typically required with searching through vaste databases, enabling better decision-making and cross-referencing of data for a better end-result to the user. This helps accelerate drug discovery and synthesizes a better use of resources and time by personnel involved.
III.3 - BioHive-2 Supercomputer
As of May 13, 2024, NVIDIA-powered BioHive-2 supercomputer, the largest supercomputer in the pharmaceutical industry, was officially announced to be completed. This supercomputer, at the time of writing, ranks number 54 on the TOP500 list of most powerful supercomputers, a metric widely used across all industries for evaluating power and performance. Compared to BioHive-1, RXRX's first iteration of supercomputer first launched in 2021, BioHive-2 quadruples its computational speeds.
RXRX has carefully thought out the development of scaling computation as the size of training data and model parameters has increased gradually over time. Using Phenom-1 as an example, a deep learning model that was debuted by RXRX earlier this year designed to simulate and analyze complex biological and chemical interactions using large-scale datasets to help generate predictions on how potential drug candidates will interact with biological systems, Phenom-1 required multiple months of computational time for experimentation and training in order to be deemed usable on BioHive-1. However, on BioHive-2, multiple AI projects or deep learning models of similar or even greater size can be run in parallel with each other in shorter timeframes, speeding up the entire pipeline of drug research and discovery by eliminitating bad candidates.
IV - Current AI-Driven Drug Discovery Pipeline for Specific Diseases
RXRX focuses primarily on precision oncology and rare diseases in the realm of its current pipeline, in line with current partnerships established.
It is important to note that most of these drugs as it relates to these diseases were discovered and brought to clinical trial in a short time frame, relative to the normal non-technologically leveraged approach that has been seen in the past. Most sources I referenced came to the conclusion that the normal pipeline of medicine, from discovery to market, typically takes anywhere from 10-15 years or more, with much longer processes involved in processes such as gene therapy, taking almost or more than double that in some cases. While the drugs for these diseases are still in the pipeline and not at the level of being available market wide, the speed that has been seen on discovery and trials is truly unprecedented, thanks largely to the AI link that RXRX provides.
On top of being able to provide faster speeds of drug delivery to markets, costs have also massively been lowered due largely in part to the AI approach that RXRX has taken. The average R&D cost, as approximated by RXRX, of a single approved medicine stemming from the normal non-technologically leveraged approach from discovery to approval is around $2 Billion, due to factors such as failure rates, slow speed of research and synthetization, and more.
Now I'm not going to sugarcoat this; The information provided regarding these drugs being developed is massively geared towards the current working medical and research professionals, both of which I am not. This section will largely be copy and paste, direct from RXRX's website and ClinicalTrials.Gov due to the nature of the science involved.
IV.1 - Oncology
Advanced Solid Tumors: REC-617 is a reversible, non-covalent small molecule CDK7 inhibitor being developed for the treatment of multiple advanced solid tumor indications. There are currently no CDK7 inhibitors approved by the FDA. The precision design of REC-617, resulting in high selectivity and optimized half-life, could distinguish it from other CDK7 inhibitors in development, by enabling the management of potential toxicities associated with CDK7 inhibition and maximizing on-target efficacy.
Biomarker-Enriched Solid Tumors and Lymphoma: REC-1245 targets RBM39, a novel CDK12-adjacent target identified by the Recursion OS. RXRX believes they can modulate this target to produce a therapeutic effect in biomarker-enriched solid tumors and lymphoma.
B-Cell Malignancies: REC-3565 is a small molecule MALT1 inhibitor, being developed for multiple hematology indications. There are currently no MALT1 inhibitors approved by the FDA. The precision design of REC-3565 has resulted in a well-balanced molecule with selectivity over UGT1A1, which distinguishes it from other MALT1 inhibitors in clinical development. Avoiding UGT1A1 inhibition can potentially reduce hyperbilirubinemia risk and allow a better combination profile with drugs that have known liver toxicity issues.
Small-Cell Lung Cancer: REC-4539 is the first LSD1 inhibitor designed to be both CNS-penetrant and reversible, and is being developed for multiple hematology and solid tumor indications, including small-cell lung cancer (SCLC) and acute myeloid leukemia (AML). There are currently no LSD1 inhibitors approved by the FDA. REC-4539’s combined properties distinguish it from other LSD1 inhibitors in development, with the potential to reduce adverse events seen from on-target platelet effects.
IV.2 - Rare
Cerebral Cavernous Malformation: REC-994 is an orally bioavailable small molecule superoxide scavenger being developed for the treatment of CCM. CCM is a devastating neurovascular disease with approximately 360,000 symptomatic patients in the US, France, Germany, Italy, Spain and the UK. In Phase 1 trials in healthy volunteers that Recursion conducted, REC-994 demonstrated tolerability and suitability for chronic dosing. RXRX's Phase 2 SYCAMORE clinical trial met its primary endpoint of safety and demonstrated encouraging trends in objective MRI-based exploratory efficacy measures at the highest dose, seeing reductions in lesion volume and hemosiderin ring size.
Familial Adenomatous Polyposis: REC-4881 is an orally bioavailable, non-ATP-competitive, allosteric small molecule inhibitor of MEK1 and MEK2 being developed to reduce polyp burden and progression to adenocarcinoma in people living with FAP. There are currently no FDA-approved therapies for the treatment of FAP, a rare tumor predisposition syndrome affecting approximately 50,000 people in the US, France, Germany, Italy, Spain and the UK.
Neurofibromatosis Type 2: REC-2282 is a CNS-penetrant, orally bioavailable, small molecule pan-HDAC inhibitor being developed for the treatment of NF2-mutated meningiomas. There are currently no FDA-approved drugs for the treatment of patients with NF2, an inherited genetic syndrome that can cause a variety of benign tumors in the central nervous system, including meningiomas. This molecule appears to be well tolerated, including in patients dosed for multiple years, and potentially has reduced cardiac toxicity that would differentiate it from other HDAC inhibitors. Its oral bioavailability and CNS penetrance distinguish it from currently approved HDAC inhibitors.
Hyphophosphatasia: REV102 is an orally available, small molecule ENPP1 inhibitor, being developed for the treatment of hypophosphatasia (HPP). HPP is a rare, potentially life-threatening genetic disease, characterized by impaired mineralization of bones and teeth. Inhibiting ENPP1 reduces inorganic pyrophosphate (PPi) levels which may restore the PPi and phosphate balance needed to promote bone mineralization. With RXRX's collaborators, RXRX have shown that ENPP1 inhibition is safe and well-tolerated by preclinical models, and for the first time demonstrated that ENPP1 is a druggable target for later-onset HPP.
IV.3 - Other
Clostridioides difficile Infection: REC-3964 represents a potential first-in-class, oral, non-antibiotic, novel small molecule approach designed to selectively inhibit the toxin produced by Clostridioides difficile in the gastrointestinal tract. This molecule has the potential, when used as part of a treatment regimen, to prevent recurrent disease and/or other forms of C. diff infection, which is a leading cause of antibiotic-induced diarrhea sometimes leading to significant morbidity and mortality. More than 29,000 patients die in the US every year from C. diff infection. REC-3964 has been well tolerated in healthy volunteers with no reported serious adverse events.
Idiopathic Pulmonary Fibrosis: Target Epsilon represents a potential first-in-class novel chemical entity for the treatment of an undisclosed indication in Fibrosis with compelling early data. Compelling activity was demonstrated in a gold standard animal model of a fibrotic disease with significant unmet need.
V - RXRX's Aqcuisition of Exscentia
Announced on August 8, 2024 and finalized on November 20, 2024, RXRX has officially acquired Exscentia, a technology-driven clinical state drug design and development company. A quote put forth by Gibson on the day of agreement in August describes this acquisition perfectly:
“We believe the proposed combination is deeply complementary and aligned with our missions to industrialize drug discovery to deliver high quality medicines and lower prices for consumers. Exscientia’s precision chemistry tools and capabilities, including its newly commissioned automated small molecule synthesis platform, will augment our tech-enabled biology and chemistry exploration, hit discovery and translational capabilities."
This acquisition positions RXRX well as, in November when finalized, the newly combined company had received $450 Million in upfront payments from current partnerships, with an additional $20 Billion in payments possible, before royalties, through the combined partners that now are contracted with RXRX and Exscentia as one entity, RXRX.
Additional data made available, as the result of the acquisition of Exscientia and data provided by them or their partners, such as Helix and Tempus, has helped expand the capabilities of Recursion OS which has helped propel it to be a first-in-class and best-in-class drug discovery and development platform, reaching a total of over 60 petabytes of proprietary data.
VI - Finances and Economics (as of 29 Dec, 2024)
VI.1 - Quick Figures
~$2.9B Market cap
+2.21% 3M
-23.66% YTD
Analysts target $10 stock price within 1 year
VI.2 - All things Stock related
323.51M Shares total
Average 30 day volume: 15.42M
Latest earnings report Q3 2024:
-0.34 EPS; This reflects RXRX's investment into its newly released BioHive-2 supercomputer and ongoing advancement of RecursionOS, with operating expenses driven by R&D efforts.
-4.84 P/E Ratio: While currently reporting net losses, this is largely due to its expansion of it's AI-driven Recursion OS platform through the BioHive-2 supercomputer and many models. Not having any currently approved and ready-for-market drugs makes this a norm for early-stage companies within the biopharm industry, as the P/E ratio, among other indicators, typically remain negative until so.
$429.2M in cash and equivalents reported in Q3 2024, an increase from $394.8M in Q4 2023. This strong liquidity ensures RXRX can sustain its operational and research commitments while executing its strategic objectives.
Annual revenue in 2023 measured $38.5M, with steady growth anticipated as partnerships and revenue streams mature in 2024 and beyond.
Net loss for Q3 2024 was -$95.8M, reflecting RXRX's strategic investments in scaling its technology and drug discovery capabilities. Despite the current losses, RXRX's expanding partnerships, its acquisition, cash on hand, and AI platform position it well for future profitability and long-term growth.
VII - Notable Visuals
All data taken directly from NASDAQ or TradingView and made into charts utilizing ChatGPT 4o model (for ease of time), verified by myself for accuracy of numbers.
VIII - Closing Remarks
VIII.1 - Risk
The investment into RXRX carries some slight risk, being an early-stage biopharm company with no current drug approvals open and available on the market. Operating at a net loss by all accounts and a negative P/E ratio, the stock has remained quite stagnant, staying below a $16 range for the almost 3 years. This largely is due to the massive amount of expenditures on research and development that comes with being an early-stage biopharm company. While the BioHive-2 supercomputer and RecursionOS remain very impressive, there is currently no guarantee that it'll be able to constantly deliver drugs quick to commercial market as advertised. Reliance on future royalties for drugs that currently do not exist also poses a threat, should they not come into fruition. Delays, cancellations, or failures of FDA-approved trials may also set RXRX back quite a bit, hurting profitability that is needed and future growth. This also applies to regulatory risks of potential drugs as tested in clinical trials. The rate of return, such as the change in the 1Y stock price, also poses a threat to potential investors, who look towards a company's historical returns when deciding where to place money with the market.
VIII.2 - Competitors
Other competitors to RXRX exist within the biopharm industry, such as, but not limited to, Schrodinger, Relay Therapeutics, and Atomwise. These companies, among others, remain highly regarded in the AI-fueled drug research and discovery space of the pharmaceutical industry.
These companies also share some partners, such as RXRX and Atomwise sharing Sanofi, creating competitiveness within the industry and between companies. Each company also boasts their own impressive portfolio of partners, who have pledged substantial amounts of fiancial backing to their own respective machine learning models to help propel them to the forefront of the industry, where RXRX currently stands.
While it seems that RXRX is at the forefront of drug research and discovery, largely in part due to their unrivaled supercomputer and RecursionOS, other competitors may have better products in the works behind closed doors that may flip the script, however, that remains to be seen.
VIII.3 - My OPINION on the future of this company
This originally started for me as just a play I saw many others follow on AfterHour, having done little research other than the basics myself. This was definitely a much bigger undertaking than my $LUNR Initiative document that I published just a week or two ago, due to the much wider, and much more convoluted, product offerings and medical/scientific/technological information that was at hand. While I consider myself pretty technologically savvy, having built more than a handful of computers in my lifetime, running a video production company with my expertise being cinematography and editing, and operating out of a home-built server (all NVIDIA GPU products of course!), I am definitely not medically nor scientifically savvy (except on the space end, I do stay updated in that industry through chats with some buddies employed in that sector, among my own research).
All-in-all, my personal opinion is that RXRX is doing some computing magic in the world of both AI and medicine. Making amazingly large strides in the realm of drug discovery through leveraging artificial intelligence, a technology so new in the grand scheme of things, from a company who is only just over 11 years old, is mindblowing. This isn't Google or Microsoft, with decades of expertise and seemingly unlimited funding to do with what they wish, but just a random company that started from humble beginnings with a small team of 6 and a laboratory made out of a utility closet. They had a vision and have set out to successfully become the premier technology-connected biopharmaceutical company.
Operating the best supercomputer within the industry, and one of the best on the planet, directly backed by quite literally the largest company on the planet, and having gathered dozens of petabytes of information on human biology down to an atomic level to discover drugs for diseases previously thought uncurable is certainly no small feat. RXRX boasts one of the broadest and most mature therapeutic pipelines for drugs involved in AI discovery and research to date, with all data existing on the RecursionOS being proprietary and most of it being in-house wet lab generated, something other comapnies cannot currently equal, especially when comparing sizes of datasets available with this in mind.
RXRX's vast array of partners, stemming from both the science/technology side of things, as well as the medical/research side of things, give it quite an edge compared to competitors.
While there are some downsides to the company, I think the future remains bright, especially due to their pioneer status within the industry. Acquiring Exscientia also was seen very favorably among all, investors and analysts alike, bolstering them into the biggest powerhouse of AI-drug research.
Currently, I only own an even 100 shares of this company, largely due to lack of capital available, but I'll be looking towards upping my share count greatly in the future and holding for the long.
By Ben, @Intenxe on AfterHour and Discord
Updates on important information can be found on the continued pages and will be posted in near-real time (I'll try my absolute best to get the pertinent info out in a timely manner.)
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