Centralising analytical data from mass spectrometry in drug discovery and development

Unlock the full value of your MS data — make it FAIR, AI-ready, and built to scale
Mass spectrometry (MS) is a cornerstone of modern drug discovery. But for many organisations, legacy systems, fragmented formats, and poor metadata are slowing progress, creating costly bottlenecks, and limiting the impact of AI/ML.
This exclusive white paper, produced by Scientific Computing World in partnership with Thermo Fisher Scientific, brings together experts from Zoetis, Bayer, LifeMine Therapeutics, Iterion, Targenomix, and the Pistoia Alliance to share how leading pharma and biotech organisations are solving these challenges.
Why download this white paper?
Discover how to:
Break down data silos by centralising and standardising mass spectrometry data across instruments and teams
Make data FAIR (Findable, Accessible, Interoperable, Reusable) to ensure long-term value and AI/ML readiness
Accelerate collaboration with CROs and external partners by adopting shared data standards
Prepare for the future with insights into AI/ML adoption, explainable AI, and even quantum computing in drug discovery
“AI is only as good as the data it learns from. Without structure, you can’t trust the output.”
— Birthe Nielsen, Pistoia Alliance
What’s inside
Real-world strategies for overcoming MS data challenges
Best practices for FAIR, AI/ML-ready data management
Case studies from leading pharma and biotech organisations
Recommendations to future-proof your data strategy