The webinar conference will last approximately 2.5 hours. If you register for the event, you will receive the recording when it is ready via email even if you are unable to attend the live event.

The conference takes place Wednesday October 30, 2024, 3-5.30pm GMT, 4-6.30pm CET, 11am-1.30pm EDT, 10am-12.30pm CDT, 8-10.30am PDT.

For each conference, we generally have 3 speakers from a variety of backgrounds such as pharma, biotech, technology, or academia. The speakers give a 30-minute presentation, then at the end, we have a roundtable Q&A session with all of the panelists.

The aim of the conference is to showcase knowledge graph use cases, tips and tricks and thought leadership to the biopharma community.

Find out more about the conference series and watch past conference recordings here.

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Agenda-- more talks to be announced soon!

An Ignorance-Base for Prenatal Nutrition: A Knowledge Graph to Explore the Literature's Known Unknowns
Mayla R. Boguslav, Research Associate, Southern California Clinical and Translational Science Institute (USC Keck School of Medicine)

Research progresses through accumulating knowledge such that a previously unexplored subject (an unknown unknown) becomes an active research area exploring the questions (known unknowns), until a body of established facts emerges (known knowns). Many knowledge-bases exists for known knowns, but no ignorance-bases exist for known unknowns. What novel connections and insights are in the unknowns? Using a knowledge graph, we created the first ignorance-base for prenatal nutrition to help find pertinent questions that could affect mothers and offspring globally.

Knowledge Graphs and Graph Neural Networks for Early Drug Target Characterization
Andrei Zinovyev, In Silico R&D Department, Evotec

Biomedical knowledge graphs serve as powerful data integration platforms and are widely utilized in early drug discovery by biotech and pharmaceutical companies. Knowledge graphs facilitate efficient retrieval of existing data and augment this with predictive inference, leveraging advanced analytical tools such as graph neural networks (GNNs). In this presentation, we will demonstrate the application of predictive approaches to two representative tasks in drug target characterization. The first application involves the analysis of high-throughput cell imaging, where GNN-based modeling aids in evaluating the novelty of experimentally determined genetic associations. The second application showcases the use of GNNs to develop predictive AI models for early drug target safety assessment (TSA), integrated into Evotec's internally developed TSA platform.

Mastering LLMs and Knowledge Graphs
Tony Seale, 'The Knowledge Graph Guy'
Tony has been passionate about data integration for well over a decade. He has widely shared his creative vision for the widespread use of Large Language Models (LLMs) and Knowledge Graphs in large organisations through his popular weekly LinkedIn posts, earning him the reputation of ‘The Knowledge Graph Guy.’

Tony's interest in AI and Knowledge Graphs began as a secret side project on a computer under his desk while working at Deutsche Bank 10 years ago. By the time he left, he had leveraged the technology to resolve an audit point for double barrier options, provide systematic intelligence across the total FX trade population, and integrate the Fixed Income Derivatives desk. Since then, Tony has delivered several mission-critical Knowledge Graphs into production for Tier 1 investment banks, including architecting the UBS Knowledge Graph.

Tony now specialises in the intersection of Large Language Models and Knowledge Graphs, a combination poised to be crucial in the next wave of AI adoption.