Lab-in-the-loop: How to reduce drug discovery timelines

Drug discovery timelines average 12 to 30 years. A lab-in-the-loop approach can compress that to fewer than 7 years by connecting real-time experimentation with machine learning to create a continuous, self-improving cycle.

This e-book explains how pharmaceutical organizations can move from fragmented R&D systems to a unified, AI-native discovery engine. You'll learn how to eliminate costly rework, recover missed insights and accelerate therapies to patients who need them.

Gain insights into:

• How lab-in-the-loop connects design, testing and optimization into one adaptive cycle
• Why 80% of scientists report that current workarounds hurt productivity and 70% say those workarounds lead to compromised decisions
• How Siemens' Luma platform unifies wet lab and dry lab workflows without replacing your existing ELN, LIMS or SDMS
• How to enable real-time data flow, adaptive workflows and end-to-end traceability across your R&D lifecycle
• How AI-driven automation preserves scientific judgment for decisions that require it

Why this matters now:

Legacy infrastructure forces scientists to choose between speed and accuracy. Siloed tools like ELNs, LIMS and SDMS' serve individual functions but fail to connect data end-to-end or deliver the scientific context needed for confident decision making. The result is slower time-to-market for life-changing therapies.

This e-book reveals how a multimodal scientific platform approach addresses these challenges by linking every step of the research cycle and ensuring information is connected and accessible in real time.

Download the e-book to discover how pharmaceutical organizations are preparing to go from preclinical through commercialization in significantly shorter timelines.