WEBINAR RECORDING AVAILABLE - WATCH ON DEMAND

Is the AI revolution empowering the scientist?

Artificial intelligence (AI) and machine learning (ML) continues to develop how we discover and overcome challenges in drug discovery and suggests a promise to transform the path to clinic. These functionalities have the capability to expedite timelines, explore areas of drug space not previously attainable, and in turn, have the potential to reduce costs of progressing a drug through early discovery. Has this idea delivered on its hype? Join us to hear perspectives from AI thought leaders and drug hunters on how AI is actually delivering, and its potential to transform the way we navigate drug discovery.

This webinar will discuss:

Confronting the challenges around AI and its adoption. Has the promised revolution caused AI to have an unsettled beginning?
What is AI/ML trying to achieve in early drug discovery? What is the ultimate goal and how should we measure success?
Why bringing together AI/ML engineers together with traditional drug hunters will transform the discovery of tomorrows therapeutics

Charles River and Valence have entered a strategic relationship in order to accelerate their clients’ drug discovery pipelines, with Charles River providing a vehicle from early discovery through development, and Valence overlaying their platform for AI-enabled drug design.

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Michael Bronstein

Professor, Imperial College and Head of Graph Learning Research, Twitter

Michael Bronstein is a professor at Imperial College London, where he holds the Chair in Machine Learning and Pattern Recognition, and Head of Graph Learning Research at Twitter. Michael received his PhD from the Technion in 2007. He has held visiting appointments at Stanford, MIT, and Harvard, and has also been affiliated with three Institutes for Advanced Study (at TUM as a Rudolf Diesel Fellow (2017-2019), at Harvard as a Radcliffe fellow (2017-2018), and at Princeton as a short-time scholar (2020)). He is a Member of the Academia Europaea, Fellow of IEEE, IAPR, BCS, and ELLIS, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019).

Jörg Wegner

Associate Scientific Director and Team Leader, AI Design Team, Janssen Research and Development

Jorg has more than fifteen years of drug design experience supporting projects across all therapeutic areas from early target identification to late lead optimization, as well as rationalizing design implications of clinical data and leading teams to support internal drug design projects as well as driving strategic AI initiatives at Janssen. He is currently Associate Scientific Director, Team Leader AI Design Team at Janssen Research & Development. He holds a PhD in Computer Science from the Centre for Bioinformatics at the University of Tübingen (ZBIT).

Grant Wishart

Director CADD & Structural Biology, Charles River

Grant Wishart PhD, Director CADD & Structural Biology joined Charles River in 2011 and leads the CADD & Structural Biology groups which provide computational chemistry, structural biology, biophysics and protein production services across drug discovery programmes from hit identification to lead optimization. Prior to joining Charles River, Grant worked for 10 years as a computational chemist in the computational medicinal chemistry group of Organon, Schering-Plough and MSD where he contributed to numerous CNS projects in the areas of pain, depression and psychiatry. After completion of his doctoral studies in modeling 5-HT receptors, Grant also spent four years in the drug discovery group at the Wolfson Institute for Biomedical Research, University College London. Grant is a co-inventor of 22 patent applications and co-author of 35 peer-reviewed publications.

moderator

Daniel Cohen

CEO and Founder, Valence Discovery

Daniel is the co-founder and CEO of Valence Discovery, an AI-enabled biotechnology company developing novel machine learning technologies for molecular design and multiparameter optimization, with an emphasis on small and sparse datasets not accessible to classical deep learning approaches. Valence is building on technologies originally developed by the company’s founding team at Mila, the world’s largest deep learning research institute. Daniel holds degrees in computer science, neuroscience, and machine learning.