Jörg Bentzien, Alkermes, Inc.

Jörg Bentzien Jörg Bentzien is a Research Fellow in the Modeling and Informatics group at Alkermes, where he is leading the chemistry efforts on an early drug discovery project and providing modeling support throughout the drug discovery organization. Before joining Alkermes Jörg Bentzien worked at Forma Therapeutics, Watertown MA, Boehringer Ingelheim, Ridgefield CT, and Xencor, Monrovia CA. Dr. Bentzien has broad experience in drug discovery working on multiple targets in different disease areas, CNS, Immunology & Inflammation, Cardiometabolic diseases. While at Boehringer Ingelheim he implemented a suite of predictive ADMET models used throughout the Medicinal Chemistry department and pushed for automatically self-updating models. Jörg Bentzien has a Ph.D. in chemistry from the University of Münster, Germany and did post-doctoral studies with Nobel Laureate Arieh Warshel at the University of Southern California.


11:35 AM - 12:05 PM

Employing AI to find the Translational Piece for Making Drugs Effective, Validated and Useful in Humans

AI tools are making drug discovery more efficient, but the real win from AI is about achieving a higher rate of translation and success rates from biology to pre-clinical and from pre-clinical to clinical programs. The use cases in this session will cover the use of AI tools in prioritizing drug candidates that will be most effective, validated and useful.

  • Brandon Allgood

    Brandon Allgood CTO and Co-Founder Numerate

    Case Studies: Improving Translation using AI from Biology to Pre-Clinical, and from Pre-Clinical to Clinical Programs

  • Heather A. Arnett, Ph.D

    Heather A. Arnett, Ph.D Vice President, Research NuMedii

    Using single cell RNAseq and AI to identify therapeutic targets in fibrosis

  • Jörg Bentzien

    Jörg Bentzien Research Fellow, Discovery Alkermes, Inc.

    This case study will cover the automation of the Design-Make-Test-Analyze cycle in early drug discovery. The focus will be on how automation can be used to make better decisions and on the concept of automatically self-updating predictive models.

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