5 R&D trends reshaping data science in 2026

AI-assisted workflows are now embedded across life sciences R&D and are becoming parts of core R&D infrastructure. This shift introduces a new challenge: governing increasingly autonomous, model-driven work with the rigor expected of regulated systems. Yet many environments were never designed for this level of scrutiny, and teams are often moving faster across fragmented tools, where lineage, reuse, and accountability are harder to defend. These pressures are driving major changes.

Read this white paper to understand the five big trends that are changing regulated data science:

• Autonomous DMTA loops become standard discovery infrastructure
• In-silico ADME and toxicology become central to preclinical decisions
• Protocol design copilots redefine accountability in clinical design
• LLM copilots in statistical computing environments are treated as production systems
• AI governance becomes a regulated operational capability