AI-powered Research Assistants: How Bayer and Pfizer accelerate Drug Discovery in Preclinical R&D

• "Has this observation been documented in previous Phase 1 studies?" 

• "Are there existing clinical studies that explain my current observation?" 

• "Can I generate an FDA/Regulatory-compliant study report?" 

• "What were the critical decisions made for this molecule?" 

• "Can I get R code for immediate data visualization and analysis?" 

If you work in the R&D department, regardless of your industry, whether it's pharmaceuticals, healthcare, life sciences, or med tech, you very likely encounter such questions on a weekly if not daily basis. 

Answering these requires not only finding the right data but also understanding the knowledge and context within the data—structured and unstructured—often spread across multiple sources. Often, this expertise is confined to the most experienced users, making it difficult to share or scale. The inability to reuse existing knowledge can be a costly affair; for instance, animal studies can range from approximately 275,000 Euros for smaller animals to upwards of 1.1 million Euros for larger ones. 

Advancements in knowledge graphs, data and AI technology have now made it possible to find and interpret knowledge more effectively, while also assisting R&D researchers in activities such as planning, monitoring, analytics, content creation, and reporting. 

Join our webinar featuring the Head of Pharmacology & Safety IT at Bayer AG, and the Director of Data Science & Machine Learning at Pfizer, alongside experts at Thoughtworks, as they delve into the AI-powered R&D Research Assistant — a specialized methodology leveraging data and AI for knowledge management. Discover how the Research Assistant operates within their organizations, the problems it addresses, the challenges encountered, the opportunities it created, and the valuable insights gained through user adoption.