Brandon Allgood, Numerate

Brandon Allgood Brandon Allgood is the CTO and cofounder at Numerate, Inc, an AI driven drug discovery company. Brandon leads research and development of Numerate’s AI platform. He also leads the data science group, is the primary technical lead on both internal and collaborative drug discovery programs and is responsible for the technology vision at Numerate. Brandon has previously served as Director of Computational Science at Numerate and as a Research Scientist at Pharmix. He received a B.S. in Physics from the University of Washington, Seattle, and a Ph.D. in Computational Physics from the University of California, Santa Cruz. Brandon has authored scientific publications in astrophysics, solid-state physics, and computational chemistry and biology and has 15 years of experience in AI, mathematical modeling, and large scale cloud and distributed computing. He advises a number of venture capital firms and start-up companies, is a founder of the Alliance for AI in Healthcare, a member of the Forbes Technology Council and a UCSC Foundation Trustee.


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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