The challenges of generalizability in artificial intelligence for ADME/Tox endpoint and activity prediction.

Journal: Expert opinion on drug discovery
Published Date: Mar 19, 2021

Abstract

INTRODUCTION: Artificial intelligence (AI) has seen a massive resurgence in recent years with wide successes in computer vision, natural language processing, and games. The similar creation of robust and accurate AI models for ADME/Tox endpoint and activity prediction would be revolutionary to drug discovery pipelines. There have been numerous demonstrations of successful applications, but a key challenge remains: how generalizable are these predictive models?

Authors

  • David Z Huang
    REHS Program SDSC, UC San Diego, La Jolla, CA, United States of America. These authors contributed equally to this work.
  • J Christian Baber
    Scientific Informatics, Global Head of Scientific Informatics, Scientific Informatics, Takeda Pharmaceuticals, Cambridge, MA, USA.
  • Sogole Sami Bahmanyar
    Computational Chemistry, Director of Computational Sciences, Computational Chemistry, Takeda Pharmaceuticals, San Diego, USA.

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