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 artificial intelligence (AI) and machine learning (ML).
– – – – –
Webinar 3: Knowledge graphs for drug discovery
Wednesday 2 November at 3pm GMT/ 4pm CET/ 11am EST
In the rapidly evolving AI space, data science organizations are increasingly highlighting knowledge graphs as a key capability for successful AI projects. These knowledge graphs organise and integrate data into schemas to allow further reasoning and the development of new knowledge. In this webinar, we will hear how knowledge graphs and AI can be used to further the field of drug discovery and development.
Presentations:
- 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
– – – – –
Previous webinars in the series:
Webinar 1: AI and machine learning for drug development
Wednesday 19 October at 3pm BST/ 4pm CET/10am EST
During this webinar, our speakers will discuss where we currently stand when it comes to AI in drug discovery and question what the next wave of drug discovery pipelines will look like.
Presentations:
- 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
Missed Webinar 1? Watch it on demand here.
– – – – –
Webinar 2: Harnessing biodata for drug discovery
Wednesday 26 October at 3pm BST/ 4pm CET/ 10am EST
In today’s world, datasets are becoming increasingly difficult to capture, manage, process and visualise. The solution may lie in in the data itself, but where? In this webinar, we will explore why networks are vital to the effective conceptualisation of biodata, and how we can leverage biodata to advance drug discovery.
Presentations:
- 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
Missed Webinar 2? Watch it on demand here.
– – – – –
Register for Biodata and AI in Drug Discovery here:
Speakers
Abhishek Pandey, Group Lead: Pharma Discovery, AbbVie
Andreas Bender, Professor of Molecular Informatics, University of Cambridge
David Raunig, Senior Director Statistics, Takeda
John Quackenbush, Professor of Computational Biology and Bioinformatics and Chair of the Department of Biostatistics, Harvard T.H. Chan School of Public Health
Uli Schmitz, Executive Director Structural Chemistry, Gilead Sciences
Peter Henstock, Machine Learning & AI Technical Lead: Combine AI, Software Engineering, Statistics & Visualization, Pfizer
Nolan Nichols, Senior Software Engineer (Bioinformatics), Maze Therapeutics
