Yoneda Labs GTM brain

Yoneda Labs combines automated wet-lab experiments and AI software to optimize chemical reactions for pharmaceutical and chemical development teams.

category

AI-enabled reaction development

market profile

  • text: Finding reaction conditions traditionally involves literature search and repeated trial and error in a lab.
  • text: Yoneda has moved beyond software alone to offer high-throughput experimentation, route scouting and library synthesis.

personas

  • title: Medicinal chemistry project leader
    pains:
    • A coupling fails despite screening familiar ligands
    • Analog libraries exceed the team's capacity to synthesize and purify compounds
  • title: Process chemistry director
    pains:
    • A route uses reagents unsuitable for a manufacturing plant
    • Low yields and impurities undermine cost and scale-up
  • title: Discovery chemistry lead
    pains:
    • Conditions that work for a model substrate can fail across real analogs
    • Choosing between plausible routes can consume months of experiments

icp

  • text: Pharma and biotech organizations running medicinal chemistry, compound libraries or discovery programs.
  • text: Chemical and pharmaceutical process development teams seeking high-yield, scalable reaction conditions.

message pillars

  • text: Compress experimental timelines through lab automation and AI.
  • text: Screen many conditions to troubleshoot a difficult reaction.
  • text: Improve yield while accounting for plant constraints.
  • text: Compare possible synthesis routes experimentally.

objections

  • objection: Will predictions work on our actual substrates?
    response: Test a representative substrate scope and compare measured yield and impurities before expanding the engagement.
  • objection: Will the reaction translate to manufacturing?
    response: Agree on acceptable reagents and impurity constraints upfront, then separately validate scale-up.
  • objection: Why outsource when we have chemists?
    response: Start with a difficult reaction or library where parallel screening can add capacity to the existing team.

competitors

  • name: IBM RXN for Chemistry
    why: Chemistry AI tool for planning and predicting reactions.
  • name: Arctoris
    why: Adjacent automated laboratory services compete for outsourced R&D budgets, with more focus on biology.

influencers

  • name: Michał Mgeładze-Arciuch
    url: https://468cap.com/yoneda-labs-raises-4-million-to-build-foundation-model-for-chemists/
    why: Founder and CEO explaining the reaction optimization problem.

keywords

  • reaction optimization service
  • high throughput experimentation chemistry
  • AI reaction condition prediction
  • medicinal chemistry library synthesis
  • process chemistry yield optimization
  • chemical route scouting

voice

  • chemist-to-chemist
  • conversational
  • speed focused

signals

  • text: A medicinal chemistry group has a difficult coupling or is expanding an analog library.
  • text: A process team is scaling a route with low yield, impurities or unsuitable reagents.

stack

  • LC-MS
  • NMR
  • prep-HPLC
  • Bayesian optimization

hiring

  • Lab Technician

pricing

model: unknown

motion

sales_led

channels

  • website inquiries
  • LinkedIn
  • research partnerships

confidence

high

Founders

  • name: Michał Mgeładze-Arciuch
    title: Founder/CEO
    role: CEO
    bio: Michał is the Co-Founder and CEO of Yoneda Labs. Michał studied Computer Science at the University of Cambridge. He has strong software engineering and Artificial Intelligence experience having worked at Jane Street and Berkeley AI Research.
  • name: Jan Oboril
    title: Founder
    role: Founder
    bio: Jan is the Co-Founder and Chief Scientist at Yoneda Labs. Jan studied Chemistry & Biology at the University of Cambridge. Has worked at Bayer and as a research chemist for institutes in Czechia and Austria. He has a lot of experience designing ML models to solve problems in Chemistry and Biology, most notably he designed and sold a model for cell segmentation. He scored 8th/10th/11th at the international Chemistry Olympiad.
  • name: Daniel Vlasits
    title: Founder/CTO
    role: CTO
    bio: Daniel is the Co-Founder and CTO at Yoneda Labs Daniel studied Computer Science at the University of Cambridge. He has strong software engineering experience having been a software engineer at Cisco, Optiver and a programming languages researcher at the University of Cambridge. He won first place at the international robotics competition Pi Wars, and ranked 5th in the Cambridge Computer Science cohort.

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