VDP-118 [Assessment]: GERO - Machine Learning Platform for Age-Related Disease Targeting & Drug Discovery

One-liner: Creating therapeutics to slow down human aging and eliminate root causes of age-related diseases

Longevity Dealflow WG team

Senior reviewers: 1 scientist, 1 biotech manager, 1 biotech entrepreneur, 1 pharma consultant

Shepherd: Todd White

Other squad members: Eleanor Davies, Alex Dobrin

Sourced by: Alex Dobrin

Project Leads: Peter Fedichev, Alex Kadet


GERO’s mission is to double human health- and lifespan within the current generation. To achieve that goal, the company trained a Large generative Model of human Health (LMH) on a large dataset of longitudinal, real-world medical histories. The model can predict future health outcomes similarly to how Large Language Models (LLMs) anticipate the next word in a sentence. However, unlike LLMs, GERO’s models are physics-based and fully interpretable, revealing the relationship between the aging process and diseases.

GERO’s research reveals that aging is a process governed by the accumulation of entropy (random, stochastic damage), and the laws of physics dictate that processes governed by entropy are technically irreversible. While numerous biotechs and academic labs are attempting to reverse the impacts of aging, GERO instead focuses on the one variable of aging that can be altered without violating the laws of physics: the speed of aging.

The company’s moonshot program is to identify the major druggable sources of this entropic damage and to develop clinically relevant therapeutics which will significantly slow down the aging process itself.


Human aging, a multifaceted and intricate process, has been a focal point of scientific research for generations. GERO, with its pioneering approach, stands at the intersection of this research, offering a fresh perspective on understanding and addressing aging. The company has identified and quantified two distinct aging phenotypes in humans: Frailty (Late-life phenotype) and True Aging (Whole-life phenotype). By doing so, GERO’s platform simplifies the complexities associated with aging, providing a clearer roadmap for interventions.

This approach allows for the development of targeted strategies that can address multiple age-associated conditions simultaneously. As the longevity market seeks innovative solutions to extend healthspan and counteract the effects of aging, GERO’s contributions are both timely and relevant. Their platform, rooted in rigorous scientific research, offers a promising avenue for those aiming to mitigate the challenges posed by aging and age-related diseases.


Aging, an inherent process in humans, leads to diminished physiological and cognitive capacities which culminate in the onset of numerous chronic diseases, such as Alzheimer’s Disease, Parkinson’s Disease and osteoporosis. These diseases not only compromise the quality of life in later years, but also pose a strain on global healthcare systems.

There is a pressing need for innovative tools, particularly those leveraging artificial intelligence (AI) and machine learning (ML), to decode the complexities of aging, identify systemic causes, and pave the way for interventions that can mitigate or reverse age-related decline. While the influx of biological data from genomics, proteomics, and other disciplines provide invaluable insights into aging, there remains a pronounced gap in the tools equipped to fully exploit this data. Conventional analytical methods fall short in analyzing vast, multidimensional datasets. Moreover, all age-related diseases are interlinked via aging process which significantly constrains drug discovery against them. GERO’s model emphasizes the irreversible nature of aging while highlighting the reversible aspects of diseases, distinguishing the two phenomena.


GERO offers a platform that employs AI and ML to analyze the vast datasets from genomics, proteomics, and related disciplines. It connects the conventional approach of studying biology at a cellular level with a macroscopic perspective, examining organism-level phenotypes across the human lifespan.

By integrating real-world data-derived Large Model of Health (LHM) with rich molecular data (such as whole-exome sequencing), GERO has unveiled novel treatments for chronic age-related diseases both in-house and in collaborations with pharmaceutical companies.

Their strategy not only aims to decelerate the aging process but also targets the foundational causes of multiple diseases, presenting a comprehensive approach to age-related health issues. As an example, please see their announcement of research collaboration against fibrosis-related diseases with Pfizer.

To drive the multifold increase in clinical trial success against age-related diseases, GERO is dedicated to delivering therapeutics which will potentially; 1. Work regardless of patient age, 2. Target only reversible conditions, 3. Ensure that the effect can be obtained in a time frame of a clinical trial.


GERO’s mission is to ‘radically slow down human aging’. Their technology, is summarised into the following bullet points:

Innovative Approach: GERO’s platform originates from the physics of complex systems and real-world human data. This unique combination allows for a more holistic understanding of aging and disease.

Targeted Interventions: GERO’s platform identifies genetic pathways that need modification to address the root cause of multiple diseases simultaneously.

Overcoming Biases: GERO’s hypothesis-free approach allows for the clustering of diseases with shared underlying biology, overcoming traditional scientific biases.

Drug Development: GERO has a robust drug development pipeline, with interventions targeting various disease clusters, including senescence, fibrotic diseases, and true aging.

Proven Results: Preliminary results, such as their immuno-senolytic asset, have shown significant improvements in lifespan and reduction of senescence markers in old mice.

Drug Development Pipeline:

Relevance to Longevity:

GERO’s mission directly aligns with the goals of longevity research. By targeting the root causes of aging, GERO’s interventions have the potential to extend healthspan, reduce the onset of age-related diseases, and improve overall quality of life.


GERO’s multidisciplinary team has over 10+ years of experience in computationally-enabled small molecule discovery. Their collective expertise spans across the physics of complex systems to cutting-edge drug discovery methodologies.


Peter Fedichev, PhD - Co-founder & CEO

Theoretical physicist, 10+ years in biotech, expert in aging research and drug design

Max Kholin - Co-founder & COO

15 years of C-level experience in biotech; background in law and finance

Alex Kadet - Chief Business Officer

Strategy & BD executive, 5+ years in biotech; background in management & consulting

Y. Melnichek - Executive Board Member

Serial AI entrepreneur with multiple exists, including acquisition by Google

A.L. Salzman, M.D - Drug Development Advisor

Founder of Inotek Pharmaceuticals, licensed technology to Genentech in a $600 million deal and successfully underwent an IPO

Slide deck

Slide Deck: link to deck DocSend

Financing and VitaDAO Funding Terms

GERO has reserved $100k for the VitaDAO community in its current fundraise to support their ongoing research and drug development efforts. These funds will be instrumental in advancing GERO’s mission and bringing their revolutionary treatments to the forefront of medical science.

With this round of funding, their key milestones will be:

  1. Validate the platform in in-vivo experiments in Pfizer’s hands and potentially extend the collaboration.

  2. To get at least one more deal with another top-5 pharmaceutical company (active negotiations).

  3. To obtain an in-vivo proof of concept that True Aging phenotype can be not only measured, but also modified with an intervention.

  4. To raise $30M+ in the next financing round . This will enable the development of pipeline assets that build towards radically slowing down human aging.


Gaining Science and Business traction: GERO has forged research collaborations with industry giants like Pfizer Inc., applying its machine learning technology platform to discover novel therapeutic targets directly within human data. This AI-driven approach addresses Pharma efficiency challenges, facilitating faster drug development, rapid identification of new indications, early pipeline diversification, and the discovery of innovative therapeutic modalities. GERO’s unique integration of physics and biotechnology opens doors for partnerships with leading pharmaceutical companies, research institutions, and tech powerhouses, amplifying its R&D capabilities.

Expansion into New Therapeutic Areas: While aging remains a central focus, GERO’s platform holds the potential to venture into other therapeutic areas, which can diversify the company’s research portfolio and its potential revenue streams.

Global Market Reach: Addressing the universal complexities of aging, GERO stands on the cusp of a vast global market. Successful breakthroughs can position the company as a trailblazer in the longevity sector, with potential outreach for a worldwide audience.


Evolution of AI-driven Drug Discovery: The rapid pace of technological advancements in AI and biotech means that today’s cutting-edge solutions might become obsolete tomorrow. Keeping up with these advancements and continuously updating their platform will be crucial.

Regulatory Challenges: Navigating the complex regulatory landscape for drug discovery and approval can be daunting. Delays or failures in obtaining necessary regulatory approvals can hinder the company’s progress and market entry.

Data Privacy and Security: Handling vast amounts of personal and medical data comes with significant privacy concerns. Any breach or misuse of this data can lead to legal repercussions and damage the company’s reputation.

Competition and Market Saturation: The AI-driven drug discovery space is becoming increasingly competitive. New entrants or breakthroughs by competitors can impact GERO’s market share and potential profitability.


GERO’s innovative approach to human aging and chronic diseases presents a unique opportunity for VitaDAO to support pioneering research with the potential for significant societal impact. Currently, GERO is the only company that simultaneously demonstrates value for the pharmaceutical industry and commits to develop therapeutics producing multifold increase in human health- and lifespan. We urge the VitaDAO community to consider this proposal favourably and join us in our mission to revolutionise the future of human health.


  1. AI Drug Development Investments Accelerated in 2020, AI Drug Development Investments Accelerated in 2020
  2. Landscape overview Q1 2023, AI for Drug Discovery https://www.deep-pharma.tech/ai-in-dd-q1-2023-subscribe#:~:text=Dynamics%20of%20Investments%20in%20AI%20in%20Pharma&text=During%20the%20last%20nine%20years,Development%20companies%20was%20%249.66B
  3. Link to slide deck, DocSend
  4. Longevity Biotech Gero Entered a Research Collaboration with Pfizer to Discover Potential Targets for Fibrotic Diseases https://www.pfizer.com/news/press-release/press-release-detail/longevity-biotech-gero-entered-research-collaboration

Senior Review Digest - Quantitative

Below is the average scores out of 5 per category from 4 reviewers, who all recommended that the project should be advanced for community feedback.

Average Scores

  • Novelty 4.3
  • Feasibility 2.8
  • Relevance 4.3
  • Science Team 4
  • Market Advantage 3
  • IP Potential 2.5
  • Conviction Score 3.1

Senior Review Digest - Qualitative

Review 1

GERO offers an interesting approach, but I would structure the funding for specific compounds and get a better deal. The valuation is too high for the current stage of development.

Review 2

The strongest aspect of the project is the PI, Peter Fedichev. In my mind, he is one of the smartest people in the longevity space. So I would bet on him to achieve any goal. The weakness is that there is no clear plan on what is the best strategy to commercialize or how their R&D platform develops novel chemical matter/IP/PreclinicalPackage for a classical drug discovery approach.

Review 3

In summary, weighing the upside potential, the pros and cons of this opportunity, I overall recommend funding this project. At this point, VitaDAO has not funded a physics-based ML platform trained on large and highly relevant data sets. Therefore, Gero complements the current portfolio. In addition, there have been remarkable exits in the drug related AI/ML field. Yet, my conviction score is lower compared to other opportunities that I have reviewed.

Review 4

For future rounds of investments, I would suggest they provide more data to back up their platform. Moreover, the compound for Batten’s disease should be left out of investor materials or it should be clear that it was not found through this platform. Otherwise I do hope this project will turn out to be a success, as it is off to a great start with their current pharma partnerships and potential future ones.

  • Agree
  • Revisions Requested (Detail in Comments)
  • Disagree
0 voters

I read this proposal, and I still have no idea what the deliverables would be. Hype it up even more until the next company buys it?

From what I can see, there are 2 leads that came out of this platform. If it’s a platform, way more validation data would be helpful. Looks like it’s in early proof of concept for the platform? How did it rank existing hits? I remember when transcriptomics was new. You’d get 1100 hits, but only 10 of them would be any good. What is the hit rate for this platform?

I see that Pfizer is intrigued, which is a good sign, as it presumably passed their diligence. But all the goals sound like sales to me.

1 Like

Very supportive of this one and can say all is going well on behalf of Pfizer

If VitaDAO can help the company generate in-Vivo data and validate novel targets it will increase the likelihood of a pharma partner licensing specific targets or potentially a buyer of the platform.


I appreciate the contributions Pfizer has made as a strategic partner to VitaDAO, including help with dealflow like this.

I am glad Pfizer is enthusiastic about this project, and that is the strongest feature I see with the project. For me to share the enthusiasm, though, I would need to see data.


That looks really interesting and also exciting new tech for an outsider like me.

Two comments, may be irrelevant, sorry:

  • Models in pharma (like LLM) could give you an edge (ie. gpt4).

  • If you find a therapy based on a set of molecules, doesn’t mean you have patents on those and anyone can replicate the treatment, so what would be the value ultimately.

Again very interesting.

Hi @bowtiedshrike, thank you for your review and questions.

From the data that we can share, in aging/senescence we used our longitudinal data from aging patients and selected 3 targets for validation. We performed the validation experiment using the biomarkers from Unsupervised learning of aging principles from longitudinal data | Nature Communications, 3 targets from the calculation and one sham target. The best target was selected for the extensive validation in National University of Singapore in Brian Kennedy’s lab. We observed mortality delay and reduction of multiple aging related phenotypes (see slide 9 in the deck). I hope this gives you a bit more context on the potential hit rate of the platform.

We used this experiment as a demo and now build collaborations to perform the validation of the platform in non-aging indications with in hands of the best target experts industry.


Hi @keepx, thank you! To your question: for our in-house drug development programs we always generate proprietary molecules. IP protection is one of the main pillars of a biotech company.


My assumption was that machine learning (based on a lot of data) could further the validity of already existing treatments or give them a novel case (it’s not like we’re missing potential good candidates already available but efficacy, counter effects and so remain a big problem I suppose).

again it’s over my head, thanks for replying :slight_smile:

You are correct! However, from the business prospective this strategy is a tough one if you want to go beyond rare diseases. In rare diseases you have so called “data exclusivity” which protects you even if you have discovered that somebody else’s molecule works in that disease.

In other cases, you should in-lincence the molecule from another company which is a very complicated process which requires lots of cash. On top of that, it is challanging to find pairs molecule-disease where the molecule still has a valid patent for enough time and the owner is willing to sell it to you.

Some companies take this path. However, we strategically decided not to pursue this business model at scale.


Thank you for pitching today Gero team, was extremely insightful.
During the pitch it was mentioned that the plan is to get access to 100M EMR by year-end through Clarivate.
My questions:
What are the sources for Clarivate’s EMRs?
What is the depth of these EMRs?
Why Clarivate vs other providers e.g. Tempus, Komodo, Optum, Flatiron?
How are we making sure that this data is fit-for-purpose i.e. that this is data that is relevant to aging?

Second point - what is the experience of the core team (not advisors) in conducting clinical trials?

Hi Michele,

Thank you for your questions.

  1. 100M+ EMR will contain: all EMRs from Clarivate that pass our scientific due diligence (TBD in #2) + CPRD + UK BioBank

  2. We are not in a position to comment on the contents of the Clarivate dataset (i.e. names of specific medical sites) as we are under an NDA. However, I can share some of the criteria for our due diligence: 40+ y.o. patients, 5+ years of longitudinal medical history for each patient.

  3. The provider was chosen after scientific and business evaluation against the competitors. Some of the companies you mention lacked the volume needed, others are focusing on only one therapeutic area, while we needed the most unbiased dataset we can get. Some of them do not share the raw data that we need, but sell only the access to their software built on top of the data. The data quality and the budget have met the criteria set in our collaboration with Pfizer. We are aware of the current market and continuously evaluate opportunities to get more data for our models.

  4. We compared the prevalence and risk of diseases between UKB and Clarivate. We examined major diseases and also looked into a relatively “rare” disease, such as fibrosis-related diseases. We regard UKBB as the gold standard for an unbiased dataset because it allows us to conduct reference genetic studies, ensuring our model accurately reflects known genetic associations with aging. The comparison results were satisfactory. To ensure we utilize as much of available data as possible, we’re now getting the access to CPRD, as their data aligns perfectly with UKBB.

  5. Yes, none of us have conducted clinical trials in the past, but we are not planning to initiate those trials immediately within this financing round. As you mentioned, experts like Prof. Gudkov and Andrew Salzman are assisting us. We’re optimistic about bringing the CT expert in-house in the next funding rounds (1.5 - 2 years from now) and have several soft commitments (including above-mentioned KOLs.)

Please let me know if this helps and I am happy to answer any further questions.

1 Like

I agree with @bowtiedshrike. This needs more info on what exactly they will be doing. However, with Pfizer on board one could assume VitaDAO’s money would go to good use. That being said, VitaDAO is performing its own independent diligence (cc @gweisha). Some basic questions such as - the lifespan data is great but if Pfizer is prioritizing not this but instead fibrotic disease, what’s the evidence there?