What is the core technology behind Deep Genomics?

Deep Genomics utilizes a 'Digital RNA Biology Platform' that combines artificial intelligence with 10^17 bytes of data. As shown on Slide 4, this framework targets the transition from DNA to protein by using Steric Blocking Oligonucleotides (SBOs) to modify pre-mRNA. The goal is to 'program' therapies for almost any genetic condition by mastering the complexity of RNA biology through digital prediction rather than traditional trial-and-error.

How does the company justify its $180M Series C valuation?

The justification lies in the platform's scalability and its transition to clinical stages. Slide 5 shows a jump from $400M in estimated peak sales in 2019 to over $5B in 2021. Furthermore, Slide 14 outlines that the funding will advance four programs into the clinic, screen 100 genes, and secure 80 patented leads, representing a massive expansion of intellectual property and commercial potential.

What therapeutic areas does Deep Genomics focus on?

According to Slide 11, the company focuses primarily on Central Nervous System (CNS) and Metabolic diseases. Specific indications listed include Frontotemporal Dementia, Niemann-Pick Disease Type C, Pediatric Epilepsy, Parkinson's Disease, Wilson Disease, and Refractory Gout. They also have four undisclosed programs in partnership with BioMarin.

How does their AI approach differ from traditional drug discovery?

Slide 7 contrasts the 'Traditional Approach,' which sequentially derisks through experimental trial and error, with 'Deep Genomics' Advantage.' Their AI Workbench predicts outcomes—such as variant biology, therapy biology, and off-target effects—up front. This 'prediction at scale' allows them to derisk the entire process before significant laboratory investment, theoretically increasing the preclinical success rate from 10% to 50% (Slide 12).

Who are the key people involved in the company?

The leadership includes Founder/CEO Brendan Frey (formerly of Microsoft and University of Toronto) and CBO Amanda Kay (formerly of Genzyme). The advisory board is particularly notable, featuring Steve Jurvetson (Future Ventures), Peter Barton Hutt (former FDA Chief Counsel and Moderna board member), and Yann LeCun (Chief AI Scientist at Facebook), as detailed on Slide 6.

Deep Genomics Pitch Deck (2021): 16-Slide Series C Deck

See all 16 slides of the Deep Genomics pitch deck — a 2021 Series C deck — with a slide-by-slide teardown of what the deck does well and where it falls short.

Deep Genomics' Series C deck is a masterclass in presenting a 'platform-to-pipeline' transition. By 2021, the company had moved from two programs in 2019 to ten programs, nine of which were first-in-class, with estimated peak sales exceeding $5 billion. The deck anchors its value proposition in the 'AI Workbench,' which leverages 10^17 bytes of data to predict drug discovery outcomes upfront, effectively derisking biology before entering the lab. The $180 million ask was specifically earmarked to expand this digital platform and advance cohorts of programs, including four expected to be in th…

Key takeaways

The company leverages a massive data moat of 10^17 bytes to power its AI-driven RNA biology platform (Slide 3).
Deep Genomics transitioned from 2 programs in 2019 to 10 programs in 2021, with 9 being first-in-class (Slide 5).
The 'AI Workbench' uses 40 machine learning predictors to perform over 1 billion predictions across every gene (Slide 9).
The platform claims to increase the probability of preclinical success from 10% to 50% (Slide 12).
The pipeline includes high-value targets in CNS and Metabolic areas, with estimated peak sales for individual programs ranging from $600M to $1.2B (Slide 11).
A strategic partnership with BioMarin is highlighted as a key validator of the platform's expansion (Slide 5).
The $180M Series C investment is explicitly tied to screening 100 genes and securing 80 targets with patented leads (Slide 14).
The team slide features heavyweights from Genzyme, Moderna, and Facebook AI, alongside a deep bench of scientific advisors (Slide 6).

Deep Genomics: A Data-First Approach to Biotechnology

The Deep Genomics Series C pitch deck, presented in May 2021, represents a pivotal moment for the company. At this stage, the narrative shifted from 'we have a cool AI tool' to 'we are a clinical-stage powerhouse with a multi-billion dollar pipeline.' The deck is structured to prove that their digital framework for RNA biology is not just theoretical but is actively producing first-in-class therapies at a rate traditional biotech firms cannot match.

Slides 1-3: The Vision and the Equation

Slide 1 introduces the leadership team, including Brendan Frey (CEO), Amanda Kay (CBO), Ferdinand Massari (CMO), and Matt Cahill (Head of Finance). The imagery of a modern lab sets the professional tone expected for a Series C round.

Slide 2 establishes the 'Why.' It posits that the digital nature of RNA therapies has sparked a revolution, but winning requires mastering the 'enormous complexity of RNA biology.' This sets up the problem that only a high-compute solution can solve.

Slide 3 provides the 'Deep Genomics Equation': Digital RNA Biology Platform x 10^17 Bytes of Data x Artificial Intelligence = Deep Genomics. The claim here is bold: 'Programming the best RNA therapies for almost any gene in any genetic condition.' By using the word 'programming' instead of 'discovering,' the company aligns itself with the high-margin, scalable world of software rather than the high-failure world of traditional biology.

Slides 4-6: The Framework and Momentum

Slide 4 illustrates the 'digital framework for untangling complexity.' It shows the biological flow from DNA to Protein, highlighting where their Steric Blocking Oligonucleotide (SBO) intervenes at the pre-mRNA level. This slide is crucial for technical due diligence, showing exactly where their 'Digital RNA Therapy' fits into the central dogma of molecular biology.

Slide 5 is the 'Traction' slide. It compares 2019 to 2021. In two years, the company grew from 2 programs to 10 programs, and from 1 RNA mechanism to 7. Most importantly, it notes a partnership with BioMarin and an increase in estimated peak sales from $400M to over $5B. It also highlights their geographic footprint: AI and Preclinical research in Toronto, and Clinical/Business development in Boston.

Slide 6 is a 'Power Team' slide. Beyond the executive team, it lists heavy-hitting advisors like Steve Jurvetson (Tesla/SpaceX board), Peter Barton Hutt (FDA/Moderna), and Yann LeCun (Facebook AI). The inclusion of a grid of photos of the entire staff at the bottom emphasizes the human capital behind the 10^17 bytes of data.

Slides 7-9: The AI Workbench Advantage

Slide 7 directly attacks the traditional drug discovery model. Traditional methods are described as 'bespoke' and 'trial & error,' where risk is managed sequentially. Deep Genomics claims to 'derisk all up front' through prediction at scale. This is the core value proposition for a Series C investor: efficiency and higher probability of success.

Slide 8 defines their 'Predictors.' The company has 40 of them, used to identify novel targets, design therapies, and predict safety/toxicity. It specifically mentions SBO predictors for protein restoration, expression increase, and knockdown.

Slide 9 visualizes the 'AI Workbench' as a flywheel. It claims 1 billion predictions covering every gene and 300,000 variants. The output is 600,000 efficacy and safety datapoints across 20,000 RNA therapies. This slide quantifies the 'Data' part of the equation from Slide 3, proving the scale of their digital operations.

Slides 10-12: Market Application and Economics

Slide 10 shows the company's trajectory from 'Mendelian Recessive' diseases (lower complexity/prevalence) to 'Complex Many Effects' diseases (high complexity/prevalence). This suggests that while they are starting with rare genetic disorders, their AI is built to eventually tackle massive, complex disease markets.

Slide 11 is the pipeline chart. It lists specific indications in CNS (Frontotemporal Dementia, Parkinson's) and Metabolic (Wilson Disease, Gout) areas. The 'Est. WW Peak Sales' column is the 'hook' for investors, showing multiple billion-dollar opportunities. It also shows four programs partnered with BioMarin, providing external validation of their tech.

Slide 12 summarizes why the AI Workbench drives 'outsized returns.' It claims to increase the probability of preclinical success from 10% to 50%. In the world of drug development, a 5x increase in success probability is a transformative economic claim.

Slides 13-16: The Ask and Future Outlook

Slide 13 provides a bar chart of the portfolio growth, projecting a steady increase in discovery, preclinical, and clinical programs through 2024. It shows the company reaching a 'tipping point' for clinical expansion.

Slide 14 is the 'Ask' and 'Use of Funds' slide. The $180M investment is intended to: expand AI predictors, screen 100 genes, secure 80 targets with patented leads, expand partnerships, and move 4 programs into the clinic (representing $4.5B in peak sales). This is a very specific and measurable set of milestones for a Series C round.

Slide 15 is a simple 'Thank You' with lab imagery, and Slide 16 is a standard promotional slide for the platform where the deck was hosted.

What Works in This Deck

The Platform-to-Pipeline Narrative: The deck successfully argues that Deep Genomics is not just a software company, but a drug development company that uses software to win. By showing a pipeline with $5B+ in peak sales (Slide 5), they move the conversation from 'R&D costs' to 'Asset value.'

Quantified Advantage: Claiming a jump from 10% to 50% success probability (Slide 12) is a bold, memorable metric. Even if investors discount it, it sets a high bar for the company's perceived efficiency.

Validation: The BioMarin partnership (Slide 11) and the high-profile advisors (Slide 6) provide the necessary 'social proof' to back up the complex technical claims.

What is Missing

Unit Economics: While the deck mentions peak sales, it does not detail the cost to bring a single program to the clinical stage compared to industry averages. A Series C investor would likely want to see how much cheaper/faster their 'AI Workbench' actually makes the discovery phase in dollar terms.

Competitive Landscape: There is no mention of other AI-driven drug discovery firms (e.g., Recusion, Exscientia, Insitro). In a crowded 'AI for Bio' space, explaining why their focus on RNA and SBOs is superior to other modalities is a missed opportunity.

Detailed Clinical Data: For a company claiming to be at a 'tipping point' for clinical expansion, the deck is light on actual data from their preclinical trials. It relies heavily on 'predictions' rather than 'results' in this specific presentation format.

What a Founder Should Copy

The 'Flywheel' Visualization: Slide 9 is an excellent way to show how data, AI, and physical lab work interact to create a competitive moat. Founders should use similar loops to show how their business gets stronger with every iteration.

Specific Use of Funds: Slide 14 is a perfect example of how to present a large round. Instead of just saying 'hiring and R&D,' they give specific numbers: 100 genes, 80 targets, 4 clinical programs. This creates accountability and a clear roadmap for the next 18-24 months.

The 'Equation' Slide: Slide 3 simplifies a very complex business into a single line. If you can't summarize your company as a simple 'A + B = C' equation, you might not have a clear enough grasp of your core value driver.

Frequently asked questions

What is the core technology behind Deep Genomics?: Deep Genomics utilizes a 'Digital RNA Biology Platform' that combines artificial intelligence with 10^17 bytes of data. As shown on Slide 4, this framework targets the transition from DNA to protein by using Steric Blocking Oligonucleotides (SBOs) to modify pre-mRNA. The goal is to 'program' therapies for almost any genetic condition by mastering the complexity of RNA biology through digital prediction rather than traditional trial-and-error.
How does the company justify its $180M Series C valuation?: The justification lies in the platform's scalability and its transition to clinical stages. Slide 5 shows a jump from $400M in estimated peak sales in 2019 to over $5B in 2021. Furthermore, Slide 14 outlines that the funding will advance four programs into the clinic, screen 100 genes, and secure 80 patented leads, representing a massive expansion of intellectual property and commercial potential.
What therapeutic areas does Deep Genomics focus on?: According to Slide 11, the company focuses primarily on Central Nervous System (CNS) and Metabolic diseases. Specific indications listed include Frontotemporal Dementia, Niemann-Pick Disease Type C, Pediatric Epilepsy, Parkinson's Disease, Wilson Disease, and Refractory Gout. They also have four undisclosed programs in partnership with BioMarin.
How does their AI approach differ from traditional drug discovery?: Slide 7 contrasts the 'Traditional Approach,' which sequentially derisks through experimental trial and error, with 'Deep Genomics' Advantage.' Their AI Workbench predicts outcomes—such as variant biology, therapy biology, and off-target effects—up front. This 'prediction at scale' allows them to derisk the entire process before significant laboratory investment, theoretically increasing the preclinical success rate from 10% to 50% (Slide 12).
Who are the key people involved in the company?: The leadership includes Founder/CEO Brendan Frey (formerly of Microsoft and University of Toronto) and CBO Amanda Kay (formerly of Genzyme). The advisory board is particularly notable, featuring Steve Jurvetson (Future Ventures), Peter Barton Hutt (former FDA Chief Counsel and Moderna board member), and Yann LeCun (Chief AI Scientist at Facebook), as detailed on Slide 6.

Cover slide of the Deep Genomics pitch deck — Series C 2021 — Deep Genomics pitch deck, slide 1 (2021)

Deep Genomics pitch deck: the facts

Company: Deep Genomics
Year: 2021
Stage: Series C
Slides: 16
Sector: Biotechnology

Deep Genomics pitch deck PDF

The full Deep Genomics deck is embedded on this page and can be read slide by slide in the browser — no download or account required. Each slide is covered in the breakdown above.

What the Deep Genomics pitch deck was used for

Deep Genomics is an AI-driven RNA therapeutics company that uses a proprietary AI Workbench to decode RNA biology and design genetic medicines. This 16‑slide pitch deck was used for the company’s **Series C** fundraising in **2021**, in which it raised **$180M** led by SoftBank Vision Fund 2. The deck positions Deep Genomics as transitioning from a discovery platform to a clinical-stage pipeline powered by extremely large-scale data (on the order of 10^17 bytes) and dozens of machine learning predictors. The funding was earmarked to expand the AI Workbench, scale the pipeline toward roughly 30 programs, and advance multiple RNA therapy candidates into the clinic.

Business model: Biopharmaceutical company developing RNA-based genetic medicines using an AI-driven discovery and development platform focused on decoding RNA biology.

Round: Later Stage VC / Series C, pre‑clinical trials.
Year: 2021
Raised: $180M Series C financing round announced July 28, 2021.
Lead investor: SoftBank Vision Fund 2
Investors: SoftBank Vision Fund 2, Canada Pension Plan Investment Board (CPP Investments), Fidelity Management & Research Company LLC, Alexandria Venture Investments, Amplitude Ventures, Khosla Ventures, Magnetic Ventures, True Ventures
Founded: 2014
Founders: Brendan Frey, Andrew Delong, Hui Yuan Xiong
Headquarters: Toronto, Ontario, Canada (with additional presence in Cambridge, Massachusetts / Boston).
Industry: Biotechnology / Biopharmaceuticals (AI-driven RNA therapeutics).

Raising: Expansion capital to scale the AI Workbench and grow the RNA therapeutics pipeline to around 30 programs, including several clinical-stage candidates.

Total funding: Approximately $230M+ disclosed funding, including the $180M Series C in July 2021.

Use of funds as presented: Expand the AI Workbench platform, screen a broad set of genes and variants, secure new RNA therapy targets and patented leads, grow strategic partnerships, and advance multiple programs—including a goal of four clinical programs by around 2023.

What happened after the Deep Genomics deck

Following the successful closing of its $180M Series C in July 2021, Deep Genomics has focused on scaling its AI Workbench and advancing a growing pipeline of RNA-based genetic medicine programs toward and into the clinic, while remaining a privately held AI‑driven biotech headquartered in Toronto with additional U.S. operations.

Closing of a $180M Series C financing round led by SoftBank Vision Fund 2, with participation from CPP Investments, Fide
Post‑Series C strategic plan to expand the AI Workbench and scale Deep Genomics’ pipeline to approximately 30 programs,
Continued evolution of the AI Workbench into what the company later described as a highly advanced AI foundation model f

What the Deep Genomics deck got right

Strong, focused narrative around AI‑driven RNA therapeutics. The deck clearly emphasizes the "digital nature of RNA therapies" and positions Deep Genomics as uniquely capable of mastering RNA biology via an AI Workbench, aligning tightly with the company’s core scientific and technological identity.
Compelling use of quantitative platform scale metrics. Slides describing the AI Workbench highlight large‑scale data and prediction capabilities—such as 1 billion predictions, hundreds of thousands of variants per gene, and hundreds of thousands of efficacy and safety datapoints—reinforcing a perception of substantial data richness and computational scale.
Clear contrast between traditional discovery and AI‑enabled derisking. The deck’s framing of traditional drug discovery as sequential experimental trial‑and‑error versus Deep Genomics’ ability to "derisk all up front" with prediction at scale offers a clear, intuitive contrast that helps investors understand the claimed efficiency advantage.
Explicit and detailed use-of-funds linked to platform and pipeline milestones. The Series C deck ties the $180M raise directly to concrete pipeline and platform goals, including expanding the AI Workbench, screening large numbers of genes, securing many high‑value targets, and moving several programs into the clinic, giving investors a tangible sense of capital deployment.
Strong investor syndicate validation of the pitch. The presence of a high‑profile lead investor (SoftBank Vision Fund 2) and reputable institutional participants (CPP Investments, Fidelity, Khosla, True Ventures, etc.) signals strong external validation of the company and its pitch.
Well-articulated data and learning feedback loop in the platform description. The deck communicates a data‑driven positive feedback loop (train predictors, generate data, iterate) that integrates AI modeling and wet‑lab experimentation, which is attractive to investors seeking evidence of a closed loop between computation and biology.

What could have been stronger

Limited linkage between platform scale metrics and concrete clinical outcomes. The OCR text and secondary breakdowns indicate very large headline numbers (e.g., 1 billion predictions, 300,000 variants per gene, 200M RNA therapies) but do not clearly tie these metrics to realized clinical outcomes or approved drugs within the deck itself, which could leave investors questioning how platform scale translates into de-risked, value‑creating assets.
Comparative differentiation vs. other AI‑biotech platforms is not fully quantified. While the deck highlights "prediction at scale" and the ability to derisk all up front, it appears to provide relatively little quantitative benchmarking versus alternative AI‑drug discovery or RNA therapeutics platforms (e.g., comparative hit rates, cycle times, or cost reductions), potentially weakening the differentiation argument for investors familiar with a crowded AI‑biotech landscape.
Limited visibility into regulatory and clinical development strategy. The slide text references goals such as "4 in clinic by 2023" and multiple preclinical programs, but from what can be seen, the deck does not deeply detail regulatory strategy, trial design, or risk management for RNA therapies (e.g., off‑target effects, delivery challenges), which may leave sophisticated investors with unanswered clinical and regulatory questions.
Insufficient transparency on historical validation and failure data. The focus on an AI Workbench and massive prediction counts can overshadow evidence of biological validation; the OCR text suggests references to "efficacy and safety datapoints" but does not clearly break down success rates, failures, or lessons learned from prior programs, which is important for investors assessing real-world performance of AI‑guided discovery.

How an investor would read this deck

What draws attention

Scale and sophistication of the AI Workbench, including number of machine learning predictors and capacity to run billions of predictions across genes, variants, and candidate RNA therapies.
Evidence that data‑driven prediction loops (train predictors, generate data, iterate) meaningfully improve hit rates, reduce time to clinic, and lower cost compared to traditional, trial‑and‑error drug discovery.
Robustness of clinical development pipeline claims, such as having 10 preclinical programs and a path to at least 4 clinical programs by 2023, including specific indications, modalities, and trial plans.
Quality and commitment level of the investor syndicate (SoftBank Vision Fund 2, CPP Investments, Fidelity, Khosla Ventures, etc.), and how their capital and networks will support later‑stage development and commercialization.
Operational capabilities in both AI/engineering and wet‑lab biology, including ability to sustain data generation at the scales claimed and to integrate experimental feedback into model training.

Risks that stand out

Platform execution risk: scaling data generation and AI model training to billions of predictions and hundreds of thousands of variants per gene is technically complex and may encounter diminishing returns, model drift, or data quality constraints.
Clinical translation risk: even with strong in silico and preclinical data, RNA therapies (including steric‑blocking oligonucleotides) may face unforeseen toxicity, off‑target effects, or delivery barriers, impacting success rates in human trials.
Competitive risk: multiple AI‑enabled biotech companies and RNA therapeutics players are pursuing similar spaces; without clear clinical wins or unique IP, Deep Genomics could face intense competition for targets, partners, and talent.
Regulatory and reimbursement risk: novel AI‑designed RNA therapies may require substantial dialogue with regulators and payers to establish evidentiary standards and value frameworks, potentially extending timelines and affecting commercial uptake.

Questions this deck invites

How has the AI Workbench historically performed in terms of conversion from in silico predictions to experimentally validated hits and ultimately to IND‑enabling packages? Provide quantitative track records by program and indication.
Which specific genetic diseases and RNA targets are being advanced in the 10 preclinical programs, and what is the current status (e.g., lead optimization, toxicology, regulatory interactions) of each?
How are off‑target effects, immunogenicity, and delivery challenges for steric‑blocking oligonucleotides being assessed and mitigated within the platform’s design and validation workflow?
What are the key assumptions behind the projection of having 4 programs in clinic by 2023, and how have those timelines and milestones evolved since the Series C round closed?
What intellectual property protections exist around the AI Workbench (models, data, processes) and the RNA therapies themselves, and how does this support long‑term defensibility against competing AI‑driven drug discovery platforms?

What founders can take from the Deep Genomics deck

Anchoring a pitch around a clearly branded technological core (the "AI Workbench") helps investors understand the central engine driving discovery and development, especially when combined with concrete claims about decoding RNA biology.
Quantifying platform activity with large, specific metrics (e.g., billions of predictions, hundreds of thousands of variants, hundreds of thousands of efficacy and safety datapoints) can powerfully signal scale and data richness to investors.
Explicitly contrasting traditional sequential derisking with up‑front, AI‑driven prediction at scale clarifies how a new approach may reduce time and cost compared to conventional drug discovery.
Tying the use of funds to specific pipeline expansion targets (e.g., scaling to ~30 programs and moving multiple therapies into the clinic) gives investors a clear sense of what progress the round is expected to finance.
Showcasing both computational and experimental capabilities (prediction loops plus wet‑lab data generation) can reassure investors that an AI‑first biotech has the biological depth needed for clinical translation.
Highlighting a strong syndicate of long‑term, deep‑pocketed investors in the deck can reinforce confidence in the company’s ability to navigate long development timelines typical of biotech.

Deep Genomics pitch deck: common questions

What does Deep Genomics do?

Deep Genomics develops RNA-based genetic medicines using an AI platform (the AI Workbench) that decodes RNA biology to find novel targets, mechanisms, and therapeutic oligonucleotides, particularly steric‑blocking oligonucleotides for rare genetic diseases.

How much did Deep Genomics raise in its Series C and who led the round?

Deep Genomics raised a **$180M Series C** round announced July 28, 2021. The round was led by **SoftBank Vision Fund 2** with participation from CPP Investments, Fidelity Management & Research Company LLC, Alexandria Venture Investments, Amplitude Ventures, Khosla Ventures, Magnetic Ventures, and True Ventures, among others.

What was the Series C funding intended to be used for?

According to the company’s Series C press release and legal transaction summaries, the capital was intended to expand the AI Workbench, scale Deep Genomics’ pipeline to around 30 programs, and move multiple RNA therapies into the clinic, with several programs expected to be in clinical trials by 2023.

When was Deep Genomics founded and by whom?

Deep Genomics was founded in **2014** in Toronto, Ontario, Canada, by **Brendan Frey**, **Andrew Delong**, and **Hui Yuan Xiong**. Its headquarters remain in Toronto, with additional offices/operations in Cambridge, Massachusetts / Boston.

What is notable about Deep Genomics’ Series C pitch deck?

The pitch deck referenced is a 16‑slide investor presentation used to raise the $180M Series C in 2021. It emphasizes the digital nature of RNA therapies, the AI Workbench that predicts drug discovery outcomes up front, and data‑driven prediction loops that enable billions of in silico evaluations of variants and therapies.

Sources

Funding and outcome facts on this page were researched on 2026-08-22 from the pages below.

What the investor wrote

Investor-side writing matched to this company through dated, cited funding evidence.

Future Ventures · Steve Jurvetson

Same round

This investor wrote about the same funding round covered by this deck.

Series C · $180M · July 28, 2021

SoftBank led Deep Genomics' $180 million Series C round, with participation from Fidelity and the Canadian Pension Plan.
“Deep from Softbank led the $180 Series C Round of Deep Genomics, with Fidelity and the Canadian Pension Plan joining.”
Written at the time of the investment · Source
Identifying novel targets and treating genetic diseases with programmable RNA therapeutics represents one of biotech's largest opportunities.
““The potential to identify novel targets and treat genetic diseases with programmable RNA therapeutics is one of the most significant opportunities in biotech.” —Deep Nishar, Senior Managing Partner at SoftBank.”
Written at the time of the investment · Source
Deep Genomics has 10 active programs and plans to expand to 30 preclinical programs by 2023.
“They’ve now got 10 programs in the pipeline and expect to have 30 programs in preclinical research by 2023.”
Written at the time of the investment · Source
Future Ventures led Deep Genomics' previous investment round and secured a seat on its board.
“Future Ventures led the prior round, and Maryanna joined the board at that time.”
Written at the time of the investment · Source
Deep Genomics achieves a distinct advantage through its dedicated focus on RNA biology.
“Frey explains that what gives Deep Genomics its edge is its laser focus on RNA biology.”
Written at the time of the investment · Source

Deep Genomics pitch deck slides

Deep Genomics pitch deck slide 1 of 16 — Deep Genomics pitch deck — slide 1 of 16

Deep Genomics pitch deck slide 2 of 16 — Deep Genomics pitch deck — slide 2 of 16

Deep Genomics pitch deck slide 3 of 16 — Deep Genomics pitch deck — slide 3 of 16

Deep Genomics pitch deck slide 4 of 16 — Deep Genomics pitch deck — slide 4 of 16

Deep Genomics pitch deck slide 5 of 16 — Deep Genomics pitch deck — slide 5 of 16

Deep Genomics pitch deck slide 6 of 16 — Deep Genomics pitch deck — slide 6 of 16

What each slide of the Deep Genomics pitch deck says

Slide 1

3 1 acd ’ =) a « T— CR — = \! a Brendan Frey, Founder and CEO | Amanda Kay, CBO | Ferdinand Massari, CMO Matt Cahill, Head of Finance & Business Operations

Slide 2

The digital nature of RNA therapies has sparked a revolution CCCAAATGCACTCCTGG Winning will require mastering the enormous complexity of RNA biology

Slide 3

Digital RNA : 10"75ytes x Artificial Biology Platform Data Intelligence i deep i genomics Programming the best RNA therapies for almost any gene in any genetic condition

Slide 4

A digital framework for untangling complexity Sona TA REN DIGITAL GENETIC TARGET | Steric blocking Ng —" 540) DIGITAL RNA THERAPY Pre-mRNA = = 7 DIGITAL RNA BIOLOGY mRNA Protein ~~ d »

Slide 5

Our platform is now ready for expansion 10 programs, 9 first-in-class 2 programs o = Est >$ 5B peak sales Est $400Mpeaksales © WENNNNNEED S 7 ona mechanisms 1 RNA mechanism o o Partnering to expand pipeline BiOMARIN [| Toronto BOSTON Al Platform & Preclinical Clinical & Business Research Development

Slide 7

Our digital Al Workbench predicts drug discovery outcomes up front TRADITIONAL APPROACH - SEQUENTIALLY DERISK - EXPERIMENTAL TRIAL & ERROR - BESPOKE RSk N 7 Risk < RisK Variant Mechanism Effect of Associated of Variant Therapy In with Disease Biology Vitro Minimal Off Target Effects DEEP GENOMICS' ADVANTAGE: PREDICTION AT SCALE - DERISK ALL UP FRONT - ITERATIVE LEARNING

Slide 9

Data driven prediction, positive feedback loops, and exponential growth 1 BILLION predictions Every gene 300,000 variants 200,000,000 RNA therapies TRAIN PREDICTORS & CREATE NEW Al 80 machine leaming pecictors CAUSAL prediction Al WORKBENCH GENERATE DATA 600,000 efficacy and safety datapoints 250 genes 20,000 RNA therapies 10 preclinical today Apartnersd 4 in clinic by 2023

Slide text above is read directly from the Deep Genomics deck PDF embedded on this page.