Macromoltek GTM brain

category

market profile

• text: Therapeutic antibody teams use computational modeling to narrow candidates and support experimental discovery.
• text: Antibody design competes across in-house experimental platforms, modeling software and specialist AI partners.

personas

• title: Head of antibody discovery
  pains:

  • Conventional screens struggle with difficult targets
  • Lead discovery consumes expensive experimental cycles
• title: Computational biology or protein engineering lead
  pains:

  • Needs structural predictions and epitope insight
  • Must balance binding properties with developability
• title: Biotech R&D executive
  pains:

  • Small internal teams lack specialized antibody design capacity
  • Needs to advance candidates with constrained runway

icp

• text: Biotech companies developing antibody therapeutics from target identification through lead optimization.
• text: Pharmaceutical research groups needing external computational antibody engineering for specific targets.

message pillars

• text: Accelerate discovery with computational modeling and AI.
• text: Improve structural insight and cut experimental iteration.
• text: Offer a tailored scientific partnership for each target.

objections

• objection: Will predicted binders work in the lab?
  response: Review target-specific validation, proposed assays and experimental milestones before committing to a program.
• objection: How will our team retain control of the program and IP?
  response: Define collaboration scope, deliverables, data access and ownership in the partnership agreement.
• objection: We already have an internal discovery platform.
  response: Assess a difficult target or structure modeling bottleneck where external design can complement existing workflows.

competitors

• name: EVQLV
  why: Computational antibody discovery company listed alongside Macromoltek.
• name: MAbSilico
  why: Competes in computational antibody discovery and analysis.
• name: Schrödinger
  why: Its BioLuminate software supports computational antibody modeling.

influencers

• name: Monica Berrondo
  url: https://www.linkedin.com/in/monicaberrondo
  why: Founder and antibody modeling scientist with publications on antibody structure and design.

keywords

• computational antibody design
• AI antibody discovery
• antibody structure modeling
• antibody lead optimization
• antibody epitope mapping
• difficult target antibody discovery

voice

• technical
• research-oriented
• collaborative

signals

• text: Biotech announces a new antibody target or lead optimization program.
• text: A pharma team seeks external protein engineering for difficult targets.

pricing

motion

channels

• SEO
• LinkedIn
• scientific publications
• events

confidence

Founders

• name: Monica Berrondo
  title: Founder/CEO
  role: CEO
  bio: Monica Berrondo, Ph.D., is an entrepreneur, scientist, and software engineer. Her expertise lies in antibody structure-prediction and analysis, protein functional analysis, protein mutational analysis, and computational approaches to complex scientific problems.
• name: Susana Kaufmann
  title: Founder/CTO
  role: CTO