If you’ve ever tried to work on a document with multiple people, you know the struggle. One person edits the file, another makes changes, and suddenly you have:
📂 Report_v2(ReallyFinal).docx
📂 Report_v2(ActuallyFinal).docx
📂 Reportv2.2(FinalFINAL).docx
📂 Report_v2.2.1(1)(1).docx
It’s chaos.
Now, imagine that, but instead of a simple report, it’s the discovery of a new life-saving drug. Scientists working across different labs, in different countries, with different datasets, all trying to keep track of their experiments. Mistakes happen. Data gets lost. Progress slows.
But what if there was a way to keep track of every change, never lose data, and seamlessly collaborate across teams?
That’s exactly what Git does.
At its core, Git is a version control system. Think of it like a time machine for any project—whether it’s software, scientific research, or even writing a book.
Instead of saving multiple copies of files, Git tracks every single change you make and lets you go back to any previous version at any time. If you make a mistake, you can simply rewind to an earlier version—no harm done.
Git also enables collaboration. Multiple people can work on the same project at once, contributing their own updates without overwriting each other.
Git itself is just the engine. To make it truly powerful, there are platforms like GitHub, GitLab, and Bitbucket—cloud-based services that make collaboration seamless.
GitHub, for example, is where millions of developers store, share, and improve software projects together. Imagine bringing that same approach to biotech where every experiment is shared, refined, and built upon in real time.
For software developers, Git is essential. It’s how teams build and improve applications every day. But what if we applied the same principles to drug discovery?
Drug discovery is a slow, complex process. Scientists test molecules, analyze data, write reports, and refine their work over months—sometimes years. Traditionally, this information is scattered across spreadsheets, emails, and documents that aren’t always in sync.
This is where Git changes everything.
By treating drug discovery like a software project, we can bring the same level of precision, collaboration, and speed that software engineers rely on.
🔬 Experiments as Code – Every experiment on every potential drug, every hypothesis, and every result is stored in Git in common formats. Scientists and AI agents track progress like developers track code, ensuring nothing is ever lost.
📜 Regulatory Documents as Code – Writing an IND (Investigational New Drug) application is one of the most complex steps in getting a drug approved. But what if AI could generate, update, and refine these documents within a Git-controlled system? Pharma-as-code means that regulatory paperwork is automated, versioned, and constantly improving.
🤖 AI + Git – At AgentCures, our agent commits its work to Git. Scientists can then review, tweak, and refine AI-generated insights alongside AgentCures. AgentCures reacts to human contributions to the Git repository.
⚙️ Transparent, Traceable Progress – Every change is logged. Anyone can see what was done, when, and why. No more lost data or confusion over which version is correct.
This is pharma as code: