SeedBacked by Index Ventures

Cradle

Building AI for Protein Design

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Cradle

Description: Building AI for Protein Design

Investors: Index Ventures

Reference Link to Deck: https://www.businessinsider.com/cradle-biotech-startup-raises-54-million-in-fresh-funds-2022-11?r=US&IR=T

Stage: Seed

Part 1 — Slide Transcriptions

Slide 1 — Title

  • Design better proteins
  • URL: https://www.cradle.bio
  • Cradle
  • Slide 2 — Our Team

  • Bringing together the best in industrial biology, machine learning and user experience design.
  • Locations: Delft & Zürich
  • Team members listed with bios (e.g. Google Research, Amazon, ETH Zurich, TU Delft, etc.).
  • Slide 3 — Advisors & Angels

  • Investors: Index Ventures, Kindred Capital
  • Angels: Federico Ming (Frontier Bio), Emily Leproust (Twist Bioscience), Felix Streichsbier, Tom Isett (Perfect Day), Steve Crossman, Sylvain Gariel (DNA Script), Merck Grosman (DeepMind).
  • Slide 4 — Mission

  • We are on a mission to make programming biology easy.
  • Starting with finding, understanding and improving proteins & enzymes.
  • Slide 5 — Vision: Factories of Life

  • Imagine if biology could help us produce almost anything.
  • Cells are small factories. By changing DNA/RNA and optimizing proteins & enzymes, bioengineers can design optimized processes.
  • Sectors illustrated: Pharma, Chemicals, Materials, Food & Agriculture, Biotechnology.
  • Slide 6 — Market Potential

  • Next, imagine the market of “almost anything”
  • Economic impact: $2–$4 trillion from bioengineered products (covering ~60% of economic inputs).
  • Bar chart shows high vs. low estimates by industry: Health, Food & Agriculture, Consumer, Materials & Energy, Other.
  • Slide 7 — What is “almost anything”?

  • Categories:
  • Pharma (antibodies, gene therapies, vaccines, cytokines).
  • Chemicals & Materials (water treatment, detergents, corrosion inhibitors, degradable plastics).
  • Food & Agriculture (nutrition, insecticides, dairy, alternative protein, oils).
  • Slide 8 — Challenge

  • Proteins are incredibly powerful but hard to design & engineer with today’s tools.
  • Slide 9 — Current State

  • >95% of designs fail with current methods.
  • Expensive iterative process: 10–100 cycles, 1000s candidates, $150 per candidate.
  • Costs noted: <$20k (low) → $5–10m (medium) → $40m+ (high).
  • Slide 10 — Solution: Generative ML

  • We use generative machine learning models to engineer better proteins.
  • Like DALL·E or GitHub Copilot, but for protein design.
  • Illustrated benefits: 3D Structure Prediction, Thermostability, Codon Optimization, Activity, Specificity, Stability, Affinity.
  • Slide 11 — Product UI

  • Screenshots of the Cradle product interface with protein projects displayed.
  • Slide 12 — Collaboration

  • Share your results with colleagues – or the world.
  • Notebook-style interface: share, reuse templates, or attach to research papers.
  • Slide 13 — Growth Flywheel

  • We will grow this business through better predictions, cheaper experiments, sharing & publishing.
  • Flowchart: More data → Better designs → Faster experiments → More users → More projects.
  • Slide 14 — Why Now?

  • Demand for bio-based products increasing.
  • Cost of experimentation going down.
  • ML outperforming biologists in prediction.
  • Market & investment in space growing rapidly.
  • Slide 15 — Business Model

  • We monetize the studio and common bio-services.
  • Studio: software available to academia, startups, enterprise.
  • Experiments & Experts: on-demand services for faster projects.
  • Slide 16 — Closing

  • Thank you!

  • Part 2 — Design & Framing per Slide

    Slide 1 — Title

    Minimal white background with large black typography. Immediate clarity: Cradle’s focus is “better proteins.” Clean and scientific.

    Slide 2 — Team

    Grid layout of headshots + bios. Anchors credibility with experience at Google, Amazon, ETH Zurich, TU Delft. Location noted to signal hubs of biotech/ML talent.

    Slide 3 — Advisors & Angels

    Two bold colored blocks for investors, right column for notable angels. Frames strong backing from both venture funds and scientific leaders.

    Slide 4 — Mission

    Simple, bold statement with large typography: “make programming biology easy.” Subtext anchors it to proteins/enzymes. Sets an ambitious but focused north star.

    Slide 5 — Vision

    Factory conveyor belt imagery with industries labeled (Pharma, Chemicals, Materials, Food, Biotech). Visual metaphor: biology as a universal production line.

    Slide 6 — Market

    Bar chart comparing high vs. low estimates, across multiple industries. Uses economic framing to convey “trillion-dollar” potential. Anchors credibility in McKinsey/academic data.

    Slide 7 — What is “anything”

    Three-column breakdown (Pharma, Chemicals, Food). Simple typography. Expands abstract “almost anything” into concrete product categories.

    Slide 8 — Challenge

    Large, playful typography (“Proteins are incredibly powerful but hard...”). Uses wavy underline design element to visually emphasize difficulty.

    Slide 9 — Current State

    Process flow diagram + failure rates. Frames today’s methods as slow, expensive, ineffective. Sets up rationale for disruption.

    Slide 10 — Solution

    Colorful diagrams of protein folding, stability, codon optimization. Explicit analogy to DALL·E/Copilot anchors concept in widely understood ML tools.

    Slide 11 — Product UI

    Screenshots of actual platform. Establishes credibility that this is not just vision but already a functioning product.

    Slide 12 — Collaboration

    UI mockup with sharing options. Framing: not just a tool, but collaborative and scientific-community-ready.

    Slide 13 — Growth Flywheel

    Flow diagram: Data → Designs → Experiments → Users → Projects. Classic SaaS growth framing applied to biotech.

    Slide 14 — Why Now

    Four icons with short text blurbs (demand, cost, ML, investment). Design makes urgency and timing obvious.

    Slide 15 — Business Model

    Two-panel visual (Studio vs. Experiments). Clear separation between software revenue (scalable) and services (expert-driven).

    Slide 16 — Closing

    Minimal “Thank you!” with soft gradient background. Clean exit.

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