Heather A. Arnett, Ph.D, NuMedii

Heather A. Arnett, Ph.D Heather is leading research discovery and development at NuMedii, successfully translating AI hits into novel therapeutics. She has more than 15 years of experience in large and small molecule drug discovery and development, and more than 30 publications in top-tier journals with research focused on diverse pathways in immunology, neuroscience, and fibrotic diseases. In her most recent role at Amgen, Heather spearheaded multiple immune modulatory therapies, as well as building and managing a robust team of scientists and associates focused on filling the pipeline with new therapies for fibrotic and inflammatory diseases, and immuno-oncology. She holds a PhD in Neuroscience from the University of North Carolina at Chapel Hill and completed a National Multiple Sclerosis Society postdoctoral fellowship at Harvard Medical School and the Dana-Farber Cancer Institute.


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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