About this event
Join us for our latest 3-part webinar series Biodata and AI in Drug Discovery to uncover how experts organise data into biological networks and knowledge graphs, and how they refashion existing drug discovery pipelines with the help of AI & ML.
Webinar 1: AI and machine learning for drug development
Wednesday 19 October at 3pm BST/ 4pm CET/10am EST
AI can impact all stages of drug development, starting from the discovery of new molecules to understanding the mechanism of action to identifying the patients that will benefit most from these drugs. This webinar will discuss where we are in the hype cycle when it comes to AI in drug discovery and what the next wave of drug discovery pipeline look like.
Machine Learning in Drug discovery: Use cases- Abhishek Pandey, Group Lead: Pharma Discovery, AbbVie
Artificial Intelligence in Drug Discovery 2022: Aspects of Validation, Data and Where We Are on the Hype Cycle - Andreas Bender, Professor of Molecular Informatics, University of Cambridge
Methods that imitate artificial intelligence - David Raunig, Senior Director Statistics, Takeda
Webinar 2: Harnessing biodata for drug discovery
Wednesday 26 October at 3pm BST/ 4pm CET/ 10am EST
Data sets are too large for traditional databases to capture, manage and process and for people to visualise. To effectively conceptualise biodata, one needs to represent them in networks. This webinar will discuss how we leverage biodata sets to advance drug discovery.
Network based medicine - John Quackenbush, Professor of Computational Biology and Bioinformatics and Chair of the Department of Biostatistics, Harvard T.H. Chan School of Public Health
Taking advantage of 3D protein ligand information in AI-driven generative compound design methods - Uli Schmitz, Executive Director Structural Chemistry, Gilead Sciences
Webinar 3: Knowledge graphs for drug discovery
Wednesday 2 November at 3pm BST/ 4pm CET/ 10am EST
This webinar will discuss how we can use knowledge graphs and AI to further the field of drug discovery and development.
Toward a better understanding of adverse events using knowledge graphs - Peter Henstock, Machine Learning & AI Technical Lead: Combine AI, Software Engineering, Statistics & Visualization, Pfizer
Maze Therapeutics applies a genetics knowledge graph to accelerate drug discovery - Nolan Nichols, Senior Software Engineer (Bioinformatics), Maze Therapeutics
Front Line Genomics is a genomics-focused media company, with a social mission to deliver the benefits of genomics to patients faster. We organise the Festival of Genomics, digital events and webinars. We also produce reports and operate a content-rich website.
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