DocGen AI is our innovative clinical document authoring platform, currently expanding to encompass over 60 document types this year. This talk will outline the latest phase of the platform’s evolution, focusing on technical considerations and user experience, particularly regarding the quality of generated sections. We will also present initial metrics on user adoption and productivity improvements.

This talk explores how Novartis leverages generative AI to harness institutional knowledge for accelerating drug discovery. By integrating data science and AI-driven insights, this approach unearths critical knowledge assets, streamlining research processes and enhancing decision-making. Attendees will gain an understanding of how AI tools facilitate knowledge discovery, reduce time-to-insight, and drive innovation in pharmaceutical research, ultimately advancing the discovery of effective new therapies.

This talk delves into AI-driven approaches for designing and predicting molecular structures, optimizing drug discovery processes, and uncovering novel therapeutic candidates. Attendees will gain insights into generative AI's transformative role in R&D, enhancing precision and efficiency in molecular and pharmaceutical sciences.

Alnylam Pharmaceuticals, a leader in RNAi-based medicines, leverages GenAI to drive innovation and accelerate the development of groundbreaking therapies. By strategically integrating GenAI across functions, Alnylam bolsters its core capabilities and strategically positions itself to harness AI as a force multiplier. Gain an understanding and insights on Alnylam's successful AI journey from aligning executives around AI goals, establishing governance, identifying and prioritizing high-impact use cases, developing and deploying a comprehensive AI roadmap. You will acquire actionable insights and strategies to initiate and drive Enterprise AI initiatives within your organization, positioning it at the vanguard of technological advancements in the industry.

This talk introduces a framework approach which explores leveraging GenAI and LLMs to identify key risk factors for large-scale pharmaceutical manufacturers. By correlating insights from both internal and external audit data, the framework enhances competitive intelligence and strengthens supply chain risk mitigation, improving manufacturing site resilience. The use of LLMs enables rapid, scalable analysis that outperforms traditional methods, offering significant cost and time savings through their ability to efficiently parse vast data volumes and improved vectorization.

The traditional drug discovery process is lengthy, costly, and fraught with failure, with development often taking over a decade and costing billions. This talk will explore how generative AI is transforming this paradigm through Insilico Medicine’s innovative machine learning platform, enabling novel target identification, small molecule design, and clinical trial prediction. Discover how generative AI is accelerating drug development and reshaping the future of medicine.

As a new therapeutic modality, cell therapy presents complex ontologies and meta-data as challenges and opportunities for the informatics community. Traditional SME-led manual curations and data annotations cannot keep up with the multi-modal data generation speed. Automating ontology enrichment with LLM-augmented named entity recognition (NER), and ontology-driven RAG for meta-data cataloging is leading scientists to a much faster data-to-insights journey. Our presentation will introduce methods using existing large language and embedding models to effectively identify relevant information in a large unstructured knowledge base across targeted scientific fields by generating a domain-specific ontology that will both focus and enhance LLM responses during researcher-initiated search.