Nabla Bio GTM brain

Nabla Bio partners with pharmaceutical and biotechnology teams to design and experimentally test therapeutic antibodies using AI.

category

AI antibody discovery

market profile

  • text: Discovery teams must balance epitope specificity, biological function and developability, particularly for membrane targets.
  • text: The company expanded its Takeda collaboration in 2025, demonstrating a multi-program pharmaceutical partnership motion.
  • text: Other AI-driven biologics platforms compete for discovery partnerships, including Absci and Generate:Biomedicines.

personas

  • title: Head of antibody discovery
    pains:
    • Conventional screens struggle to find binders to accessible epitopes on membrane proteins.
    • Early hits can fail affinity, selectivity or developability requirements.
  • title: VP of biologics R&D
    pains:
    • Repeated design and testing cycles delay decisions across multiple programs.
    • Computational candidates need evidence in relevant biological assays before investment.
  • title: Pharma business development lead
    pains:
    • Needs evidence an external discovery platform can handle priority targets.
    • Must negotiate program scope, rights and milestone economics.

icp

  • text: Pharmaceutical companies with multiple early-stage biologics programs and budget for external research collaborations.
  • text: Biotechs pursuing antibodies against difficult membrane proteins or precise epitopes, where conventional discovery has stalled.

message pillars

  • text: Make drug development more designable and less reliant on trial and error.
  • text: Pair generative design with biologically relevant experiments.
  • text: Use one integrated data, AI and experimental engine.

objections

  • objection: Do designed binders translate to functional drug candidates?
    response: Evaluate target-specific selectivity, functional assay and developability data; early results are not clinical proof.
  • objection: Will collaboration economics and IP fit our program?
    response: Scope the specific targets, rights, milestones and responsibilities with the partnership team.
  • objection: Can the platform handle a difficult target?
    response: Review evidence for relevant target classes and agree a bounded experimental proof of concept.

competitors

  • name: Generate:Biomedicines
    why: Generative protein design and pharmaceutical discovery partnerships.
  • name: Absci
    why: AI-designed biologics combined with experimental iteration.
  • name: Antiverse
    why: AI antibody discovery for difficult targets.

influencers

  • name: Surge Biswas
    url: https://www.linkedin.com/company/nabla-bio
    why: Co-founder and CEO speaking publicly about JAM-2 and antibody design.

keywords

  • AI antibody design
  • de novo antibody discovery
  • GPCR antibody discovery
  • epitope specific antibody design
  • antibody developability
  • AI biologics partnership

voice

  • Scientific and technical
  • Ambitious about making drug discovery predictable
  • Patient outcome oriented

signals

  • text: A pharma R&D team is seeking partners for hard-to-drug membrane targets or multispecifics.
  • text: A biologics program needs early evidence on binding, function and developability in one workflow.

pricing

model: unknown

motion

sales_led

channels

  • LinkedIn
  • X
  • scientific publications
  • industry events

confidence

high

Founders

  • name: Surge Biswas
    title: Founder
    role: Founder
    bio: Currently building Nabla Bio. I'm a scientist with 10+ years of experience with using synthetic biology and machine learning to engineer everything from the microbiome to proteins. I recently completed my PhD from George Church's lab at Harvard.
  • name: Frances Anastassacos
    title: Founder
    role: Founder
    bio: Currently building Nabla Bio! I've spent the last 8 years applying my chemistry training to problems in biology and specifically synthetic biology. I did my PhD at Harvard (Class 2019, BBS) and spent some time in biotech VC.

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