Ångström AI GTM brain

category

market profile

• text: Preclinical teams face experimental bottlenecks when choosing molecules and assessing their physical properties.
• text: A reported pharma solubility pilot suggests a project-based entry point, although it is not a public price list.

personas

• title: Head of computational chemistry
  pains:

  • High-accuracy calculations limit how many candidate structures can be assessed
  • Predictions must generalize to proprietary chemistry and survive experimental validation
• title: Director of pharmaceutical development or solid-state science
  pains:

  • Uncertain solubility and crystal stability can compromise formulation and development
  • Physical screening consumes scarce lab time and material
• title: Biotech discovery program leader
  pains:

  • Teams must choose which molecules justify expensive experiments
  • Scientific stakeholders need evidence before changing decision workflows

icp

• text: Pharma and biotech organizations developing small-molecule candidates with solubility, lipophilicity or solid-form questions.
• text: R&D organizations able to compare predictions against experimental measurements and commission a scientific pilot; exact employee band is unreported.

message pillars

• text: Move experiments into computation.
• text: Offer fast simulations grounded in physics.
• text: Assess more candidate structures.

objections

• objection: Will accuracy hold for our compounds?
  response: Propose a blinded benchmark against representative customer measurements and experimental variability.
• objection: Can predictions replace lab or regulatory evidence?
  response: Start with prioritization and validate each intended use with scientific and regulatory stakeholders.

competitors

• name: Schrödinger
  website: https://www.schrodinger.com/
  why: Established physics-based molecular modeling supplier competing for computational chemistry budgets.
• name: XtalPi
  website: https://www.xtalpi.com/en/
  why: Combines AI and physics in drug discovery and solid-state research, overlapping with simulation use cases.

influencers

• name: Laurence Midgley
  url: https://www.angstrom-ai.com/
  why: Cofounder and CEO, and author on the company's crystal-structure research.
• name: Gábor Csányi
  url: https://www.angstrom-ai.com/
  why: Cofounder and head of science working on machine-learning interatomic potentials.

keywords

• molecular solubility prediction
• AI molecular simulation pharma
• crystal structure prediction
• solvation free energy
• lipophilicity prediction
• machine learning interatomic potentials
• solid form stability

voice

• scientific
• technically precise
• outcome-focused

signals

• text: A development team is seeking to improve solubility or solid-form decisions across a small-molecule pipeline.
• text: A computational chemistry group is benchmarking machine-learning potentials against experimental endpoints.

stack

• CSP-MACE-Å

pricing

motion

channels

• scientific publications
• website demos

confidence

Founders

• name: Jose Miguel Hernandez Lobato
  title: Founder
  role: Founder
  bio: Miguel is co-founder of Ångström AI and Professor of Machine Learning at the Department of Engineering, University of Cambridge, UK. He has nearly 20 years of research experience in Machine Learning and his work has been cited over 15,000 times. His research in Machine Learning for Molecules has led him to build strong relationships with partners in BioTech and Pharma. Before joining Cambridge as a faculty, he was a postdoctoral fellow at Harvard University.
• name: Laurence Midgley
  title: Founder/CTO
  role: CTO
  bio: I'm the co-founder of Ångström AI which builds fast, accurate AI molecular simulations for pharma - replacing lab experiments with computation. I also love surfing, reach out if you want to go hit OB.