Martin Akerman, Envisagenics

Martin Akerman Dr. Martin Akerman is the Co-founder and CTO of Envisagenics. He is the inventor of the SpliceCore software platform, born out of his extensive research experience at the intersection of biology and data science and his vision of discovering RNA therapeutics using machine learning. Martin trained as a post doctorate fellow with Dr. Adrian Krainer from Cold Spring Harbor Laboratory, where he helped in the development of Spinraza®, the first FDA-approved RNA therapeutic for treating Spinal Muscular Atrophy. He received his PhD in Bioinformatics from Technion, Israel Institute of Technology in 2009. As a first-time entrepreneur, Martin represented Envisagenics in winning the J&J AI for Drug Discovery QuickFire challenge in 2017 and Microsoft’s Innovate.AI challenge in 2018. His goal is to combine cutting-edge computation with RNA domain expertise to develop innovative drugs for cancer and neurodegeneration.

Session

10:40 AM - 11:35 AM

R&D Use Cases

Drug Discovery is a long process that AI technologies and tools are beginning to help shorten.  The speakers in this two-part R&D Use Case session will present information on current projects where they are applying AI.  They will cover the inputs and outcomes, as well as share insights from applying AI  to their discovery projects

Moderator

Speakers

  • Ron Alfa, MD, Ph.D

    Ron Alfa, MD, Ph.D Senior Vice President, Translational Discovery Recursion Pharmaceuticals

  • Martin Akerman

    Martin Akerman Co-founder and CTO Envisagenics

    • Envisagenics’ platform, SpliceCore, uses AI to discover druggable splicing errors: Use cases in identifying and validating novel targets for triple-negative breast cancer.
  • Kate Hilyard, Ph.D.

    Kate Hilyard, Ph.D. Chief Operating Officer Healx

    The use of AI in accelerating new rare disease drugs towards the clinic
    This use case will discuss how AI enabled us to get from disease selection to clinical candidate selection for fragile X syndrome in 24 months.

    • How AI and ML predict viable treatment options

    • The importance of partnership and patient insight – FRAXA Research Foundation example

    • Preclinical proof of concept for fragile X syndrome

    • Progression to clinical trials

  • Rishi R. Gupta, Ph.D.

    Rishi R. Gupta, Ph.D. Principal Research Scientist, Data Science & Informatics AbbVie

    Boosting Drug Discovery with Machine Learning

    Applying recent advances made in Artificial Intelligence (AI) and Machine Learning (ML) methods to advance decision making in many industries has become an extremely important area of applied research. Progress in this regard will come from combining an understanding of the current and potential decision making processes specific to the industry of interest along with new and careful approaches to data capture that support training of the data using an appropriate method of choice. How machine learning tools can increase the efficiency and expediency of this decision by helping various research groups within a Pharmaceutical organization will be presented along with case studies highlighting the application of ML methods in discovery and pharmacovigilance.


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