Now in its third year, the Lab of the future survey is a collaboration between Pistoia Alliance and Lab of the Future Congress (Open Pharma Research) bringing together unique insights into how the life sciences R&D environment is evolving. Within this webinar we will provide valuable insights to the community, highlight key industry touch […]
In pharma R&D, UX design is the difference between an AI breakthrough that changes how scientists work and a promising model that never leaves the lab. Exploring how the future of AI in pharma R&D depends not just on the technology itself, but on the experience of the people using it, this webinar brings […]
FOCUS Area: AI-Driven Image Analysis in Pharma Research & Development The Life Science Informatics Forum, established in 2021, is a developing community focused on sparking discussions on leading-edge topics within the broader Life Science Informatics arena and, more specifically, the Biopharma R&D informatics sector. Location Novo Nordisk HQ, Bagsværd-Copenhagen Session Format Forum […]
Live training program comprised of 15 sessions beginning November 4, 2025 Training summary This comprehensive training program offers a deep dive into the principles and practical implementation of FAIR (Findable, Accessible, Interoperable, Reusable) data, tailored for life science researchers and technical professionals looking to implement FAIR principles, as well as heads of R&D and […]
Data Driven Innovations – November 11 & 12, 2025 Address: Convene One Boston Place, 201 Washington Street, Boston, MA 02108 Join us at our annual USA conference, in Boston, where new ideas are sparked and new initiatives are activated thanks to the power of collaboration. Polite notice, conference registration is available for members only. If […]
Persistent Challenges in Computational Biology Computational biology is an interdisciplinary field that drives innovation across drug discovery, diagnostics, and healthcare. It is fuelled by diverse data generated by new technologies (e.g. single cell, spatial omics), constant progress in computing power and rapid development of AI-ML approaches. However, translating analyses or turning models into real-world and […]
