👉 Instead of forcing scientists into rigid platforms, what if AI allowed every researcher to use the tools they prefer while pulling from a shared data layer?
I recently attended a talk by Salima Ismayilova from CDD Vault at MBC Biolabs. Her presentation highlighted how the platform is evolving beyond its core strength in chemical drug discovery by integrating AI tools like AlphaFold and layering in richer biological features. Nathan Shapiro of Pallando Therapeutics also shared how his team applies the software. One comment stood out: he still finds himself entering one-off data for colleagues, a reminder that usability remains a work in progress.
That small detail connects to a much larger challenge: the productivity crisis in drug development. Costs remain unsustainably high, success rates stubbornly low, and timelines stretch over decades. Incremental improvements to existing workflows are no longer enough. What’s needed is a radical shift in the tools and systems we rely on, and I believe AI is uniquely positioned to drive it.
In the near term, AI’s most immediate value may be in reducing the administrative burden on scientists, acting as an agentic assistant to:
- capture experimental inputs and outputs,
- analyze both structured and unstructured data,
- surface inefficiencies, and
- suggest optimized pathways forward.
By automating these high-friction tasks, we not only improve productivity but also shorten discovery cycles. And looking further ahead, AI’s real power lies in its ability to uncover hidden patterns across vast datasets, generating insights that extend beyond human intuition.
One provocative idea, inspired by the AI&I podcast with Noah Brier, is whether AI could decouple data from platforms. Imagine researchers using the tools they prefer, while AI agents pull from a central database and ensure interoperability. Instead of forcing every workflow into platforms like CDD Vault or Benchling, AI could make the ecosystem modular and more scientist-centric.
What do you think, is that the next leap in how we “do science”?
#AIinBiotech #DrugDiscovery #LifeSciencesInnovation #ProductivityCrisis #AINovaStrategy