Biotech is where AI’s scientific promise is most vivid — protein-structure prediction, molecule design — and where the regulatory burden is unusually staged: light at discovery, heavy once work feeds a regulated study. Adopting AI well means matching controls to that stage. Here’s how, and how dgm implements it. (dgm implements osFoundry, a separate company’s platform — we are not osFoundry; scientific and regulatory determinations stay with your team.)

What AI actually does for biotech

The honest framing: AI accelerates research and discovery and eases operations, with the appropriate controls scaling up as work moves from early discovery toward regulated studies. Early on it’s lightly governed; later it inherits GxP and Part 11.

High-value use cases

  • Research synthesis and literature review — distilling vast scientific literature.
  • Lab-data analysis — surfacing patterns in preclinical datasets.
  • Computational discovery — protein-structure prediction and molecule/antibody design (central to many biotechs).
  • Operations — easing the document and admin load of a research-driven company.

The staged regulatory reality

This is the key nuance for biotech:

  • Discovery is lightly regulated. Drug-discovery AI generally sits outside FDA’s regulatory-decision frameworks (FDA’s draft AI guidance explicitly excludes discovery), so early models carry a lighter touch.
  • It tightens fast. Once AI output feeds a GLP nonclinical study, an IND, or a submission, GxP and 21 CFR Part 11 attach — the model’s reliability and data provenance must be defensible and validated.
  • Validation never disappears. Even at discovery, models need wet-lab validation — AI prioritizes and accelerates, it doesn’t replace experimental confirmation. And while AI-designed candidates are reaching trials (and AI protein work earned a 2024 Nobel Prize), no AI-discovered drug is approved yet — so don’t overclaim.

dgm builds the right controls as the work matures; scientific and regulatory determinations stay with your team.

How to start

Start with research and operations support where the regulatory touch is light, and tighten validation as output moves toward regulated studies. Prove the acceleration, then expand. dgm’s assessment finds the right starting point. (Building novel AI? Note the R&D tax credit and SBIR/STTR may apply.)

How dgm helps

dgm implements osFoundry and other AI for US biotech companies — matching controls to the stage of work, from light-touch discovery to validated, regulated processes, with training included. Pricing is fixed and public: a $399 assessment and $3,999/month implementation, with no per-seat fees. If you’d rather explore the platform first, go straight to osFoundry; if you want biotech AI done right, that’s where dgm comes in.