Maximize ROI and accelerate time-to-market by choosing the right Generative AI tools. This session offers a versatile evaluation formula for professionals, especially in pharma, to navigate complexities. We'll cover key factors like data compatibility, human-AI interaction, regulatory compliance, ROI, and technical debt mitigation. Join me to create a practical roadmap for GenAI tool selection and unlock unparalleled efficiency and success.

This talk will present how patient timelines constructed using healthcare-specific Large Language Models (LLMs) significantly enhance drug discovery efforts. Our case study demonstrates how applying LLMs to Electronic Health Records (EHRs) enables the creation of detailed oncology patient timelines. This involves extracting and synthesizing chemotherapy treatment data from diverse clinical documentation—ranging from primary care and oncology notes to discharge summaries, emergency department records, and pathology and radiology reports. These timelines offer a comprehensive, longitudinal view of patient treatments, providing critical insights into treatment patterns, response rates, and progression timelines that can inform drug discovery and development efforts.

This talk will highlight how generative AI can create dynamic and mathematical models for precision medicine, enabling the simulation of patient-specific responses. We will explore a platform that generates and scales these models efficiently, optimizing therapeutic predictions. The application of these AI-driven models promises to transform precision and quantitative medicine, improving personalized treatment and clinical decision-making.

Scientific instruments now produce enormous volumes of data that exceed local processing capacity. Online analysis combined with AI/ML methods presents one way of dealing with such massive data streams, intelligently analyzing only interesting subsets or directing instruments to relevant areas of experimental space. We will explore how ML techniques enabled by the Globus platform can help life sciences researchers support distributed data management and computing pipelines to enable efficient data analysis and self-driving experiments. We will illustrate these capabilities using examples of antimicrobial peptide identification, x-ray ptychography, and small molecule screening.

This presentation will explore GenAI initiatives developed by Novartis that demonstrate how AI can streamline research processes, optimize operations, and drive scientific advancements. Key tools will be highlighted, including FairChat, which analyzes and visualizes internal data; Protein Copilot, which identifies immunogenic peptides in the literature and provides contextual summaries; Alive, which generates detailed and compliant animal licenses; and ITKnows, which summarizes targeted knowledge from scientific literature.

This primer introduces biomedical researchers to Hugging Face tools, Large Language Models (LLMs), and Generative AI. It covers the basics of LLMs, their architecture, and applications in areas such as data extraction, literature analysis, and predictive modeling. Attendees will learn practical techniques like prompt engineering, model fine-tuning, and retrieval-augmented generation (RAG). The talk provides hands-on insights into leveraging Hugging Face’s tools for applying generative AI to transform biomedical research workflows.