As life-science organizations race to adopt (generative) AI, one point begins to stand out: your AI is only as good as your data. While large language models (LLMs) offer powerful capabilities, they’re not tailored to specialized scientific data—and do need a solid data foundation. Making data Findable, Accessible, Interoperable, and Reusable (FAIR) enables AI systems […]
The online “Pharma and Life Sciences AI/ML Training Program” comprises of 9 sessions and has been developed with the expertise of academics and industry professionals. The training is designed to equip professionals with cutting-edge knowledge and skills in the application of Artificial Intelligence (AI) and Machine Learning (ML) to the pharmaceutical and life sciences industries. […]
Agenda GANs for generating novel medicines LLMs for simulating drug development Speaker Prof. Moe Elbadawi, Queen Marys University For details of our full Pharma and Life Science AI/ML Training Program and how to register, please click here.
The Pistoia Alliance is building a pharmaceutical CMC process ontology based on the ISA88/95 framework. Our aim is to standardize laboratory and plant production process recipes to establish standardized definitions, facilitate digital technology transfers and integration with execution systems to capture structured process data for material lot genealogy tracking, streamlined technology transfers, and advanced process […]
This session will explore how computer vision is driving real-world impact in pharma and life sciences, highlighting key application areas, development strategies and critical success factors. We will also examine how emerging trends are reshaping the future of innovations in this field. Agenda Introduction to computer vision Deep learning Image classification Object detection Segmentation […]
The goal of this workshop is to identify and prioritize the key obstacles in Change Management, and establish dedicated workgroups to address and resolve them. Host Anca Ciobanu, Project Manager, Pistoia Alliance Member Only Event
Agenda Introduction to multimodal deep learning Encoders for different modalities Discriminative multimodal approaches (fusion) Generative multimodal approaches Foundation models Challenges Outlook / future trends Speaker Prof. Greg Slabaugh, Queen Marys University For details of our full Pharma and Life Science AI/ML Training Program and how to register, please click here.
Agenda Introduction to target identification Knowledge extraction from literature (embedding, clustering, chatbots) Reducing the hallucinations (RAG, Graph RAG) Accessing structured data: impact on scripting and next-gen tools Speakers Prof. Dan Crowther, University of Dundee David Brett, Associate Director of LLMs, Recursion For details of our full Pharma and Life Science AI/ML Training Program […]
Focus Area – “Advancing a Scientific Framework for GenAI: Use Cases and Workforce Education” 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. Session Format Forum kick-off: greeting from […]
Available on demand from 21 February 2025, this series of 5 recorded sessions is addressed to Pharma industry: business leaders, budget holders, tech leaders and scientists. Training goal: unravel the complexities of ontologies and demonstrate their immense value in the life sciences domain by providing a general understanding of the different ontologies and their […]
Agenda LLM agents Automating DD tasks Speakers Prof. Mike Barnes, Queen Mary University Anniek Myatt, Recursion For details of our full Pharma and Life Science AI/ML Training Program and how to register, please click here.
AI/ML is ubiquitous across the pharma industry, at least as a label. Do all AI/ML activities actually represent artificial intelligence? We will first frame AI and ML separately, and describe why we might want one or the other. With a definition in place, we will highlgiht examples of ML/AI applications across the entire drug discovery […]
