Use of artificial intelligence to enhance phenotypic drug discovery.

Journal: Drug discovery today
Published Date: Apr 1, 2021

Abstract

Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.

Authors

  • Sophia Malandraki-Miller
    Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
  • Paul R Riley
    From the Institute of Cardiovascular Science (R.C.L., V.K.K., R.E.F., N.H.C., R.P.H., P.J.T., P.D.L., P.M.E., L.C.) and Metabolism and Experimental Therapeutics, Division of Medicine (R.B.), University College London, United Kingdom; European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, United Kingdom (P.R., D.O.-S.); Gene Ontology Consortium (P.R., T.Z.B., D.O.-S., J.A.B., D.P.H.); The Zebrafish Model Organism Database, University of Oregon, Eugene (D.G.H.); Rat Genome Database, Human Molecular Genetics Center, Medical College of Wisconsin, Milwaukee (S.J.F.L.); Arabidopsis Information Resource, Phoenix Bioinformatics, Fremont, CA (T.Z.B.); FlyBase, University of Cambridge, United Kingdom (S.T.); Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME (J.A.B., D.P.H.); Oxbridge BHF Centre of Regenerative Medicine, Department of Physiology, Anatomy and Genetics, University of Oxford, United Kingdom (P.R.R.); and William Harvey Heart Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (A.T.).

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