Investor Guide
Scientific Due Diligence
What separates a credible scientific case from an optimistic one, and how to evaluate it without a PhD sitting across the table from you.
Scientific due diligence is the part of evaluating a life sciences startup that most differentiates it from evaluating a software company: the question is not just whether the team can execute, but whether the underlying biology, chemistry, or engineering actually does what the company claims. Getting this wrong is expensive — capital committed to a program whose science does not hold up rarely comes back.
In practice, scientific due diligence comes down to a handful of repeatable questions: Is the mechanism characterized well enough that the "why it should work" argument is more than plausible storytelling? Has the key result been reproduced beyond a single dataset, operator, or model system? Is there a named result that would cause the team to change direction — or has every experiment so far conveniently confirmed the thesis? And does the intellectual property actually cover the thing that creates the value, not just something adjacent to it?
A useful diligence process does not require re-running the science yourself. It requires knowing which questions to ask, and reading the founder's answers — including how they talk about the limitations of their own data — as carefully as the data itself.
Do I need a scientific background to invest in biotech?
It helps, but it is not required. What matters more is access to a credible independent read on the science — through a co-investor, an advisor, or a network like ChaosBio that applies a consistent, operator-informed framework to every opportunity it shares.
How does ChaosBio approach scientific due diligence?
Every company is read against the same questions, informed by a pharmaceutical R&D background: is the mechanism characterized rather than assumed, is the evidence reproduced beyond a single dataset, and is the development plan sequenced so the most decisive risk is retired first.
What red flags come up most often?
Results that rely on a single figure or dataset without replication, a development plan that does not name what would change the team’s mind, and founders who cannot explain what a negative result would mean for the program. See What Angels See as Red Flags on the Journal for the fuller list.
See the diligence, not just the pitch.
Every opportunity shared through the ChaosBio Investor Network comes with a memo that states the scientific risks as plainly as the opportunity.