Chemical Space Mimicry for Drug Discovery.

Journal: Journal of chemical information and modeling
Published Date: Apr 24, 2017

Abstract

We describe a new library generation method, Machine-based Identification of Molecules Inside Characterized Space (MIMICS), that generates sets of molecules inspired by a text-based input. MIMICS-generated libraries were found to preserve distributions of properties while simultaneously increasing structural diversity. Newly identified MIMICS-generated compounds were found to be bioactive as inhibitors of specific components of the unfolded protein response (UPR) and the VEGFR2 pathway in cell-based assays, thus confirming the applicability of this methodology toward drug design applications. Wider application of MIMICS could facilitate the efficient utilization of chemical space.

Authors

  • William Yuan
    Trinity College, University of Oxford , Oxford OX1 3BH, United Kingdom.
  • Dadi Jiang
    Department of Radiation Oncology, Stanford University School of Medicine , Stanford, California 94305, United States.
  • Dhanya K Nambiar
    Department of Radiation Oncology, Stanford University School of Medicine , Stanford, California 94305, United States.
  • Lydia P Liew
    Auckland Cancer Society Research Centre, Faculty of Medical and Health Sciences, The University of Auckland , Auckland, New Zealand.
  • Michael P Hay
    Auckland Cancer Society Research Centre, Faculty of Medical and Health Sciences, The University of Auckland , Auckland, New Zealand.
  • Joshua Bloomstein
    Department of Radiation Oncology, Stanford University School of Medicine , Stanford, California 94305, United States.
  • Peter Lu
    Department of Radiation Oncology, Stanford University School of Medicine , Stanford, California 94305, United States.
  • Brandon Turner
    Department of Radiation Oncology, Stanford University School of Medicine , Stanford, California 94305, United States.
  • Quynh-Thu Le
    Department of Radiation Oncology, Stanford University School of Medicine , Stanford, California 94305, United States.
  • Robert Tibshirani
    Department of Statistics, Stanford University , Stanford, California 94305, United States.
  • Purvesh Khatri
    Department of Radiation Oncology, Stanford University School of Medicine , Stanford, California 94305, United States.
  • Mark G Moloney
    Chemistry Research Laboratory, University of Oxford , Oxford OX1 3TA, United Kingdom.
  • Albert C Koong
    Department of Radiation Oncology, Stanford University School of Medicine , Stanford, California 94305, United States.

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