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An open-source drug discovery platform enables ultra-large virtual screens

Author

Listed:
  • Christoph Gorgulla

    (Harvard University
    Harvard University
    Dana-Farber Cancer Institute)

  • Andras Boeszoermenyi

    (Harvard University
    Dana-Farber Cancer Institute)

  • Zi-Fu Wang

    (Harvard University)

  • Patrick D. Fischer

    (Harvard University
    Dana-Farber Cancer Institute
    Saarland University)

  • Paul W. Coote

    (Harvard University
    Dana-Farber Cancer Institute)

  • Krishna M. Padmanabha Das

    (Harvard University
    Dana-Farber Cancer Institute)

  • Yehor S. Malets

    (Enamine
    National Taras Shevchenko University of Kyiv)

  • Dmytro S. Radchenko

    (Enamine
    National Taras Shevchenko University of Kyiv)

  • Yurii S. Moroz

    (National Taras Shevchenko University of Kyiv
    Chemspace)

  • David A. Scott

    (Harvard University
    Dana-Farber Cancer Institute)

  • Konstantin Fackeldey

    (Zuse Institute Berlin
    Technical University Berlin)

  • Moritz Hoffmann

    (Freie Universität Berlin)

  • Iryna Iavniuk

    (Enamine)

  • Gerhard Wagner

    (Harvard University)

  • Haribabu Arthanari

    (Harvard University
    Dana-Farber Cancer Institute)

Abstract

On average, an approved drug currently costs US$2–3 billion and takes more than 10 years to develop1. In part, this is due to expensive and time-consuming wet-laboratory experiments, poor initial hit compounds and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening has the potential to mitigate these problems. With structure-based virtual screening, the quality of the hits improves with the number of compounds screened2. However, despite the fact that large databases of compounds exist, the ability to carry out large-scale structure-based virtual screening on computer clusters in an accessible, efficient and flexible manner has remained difficult. Here we describe VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we prepared one of the largest and freely available ready-to-dock ligand libraries, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. One of the lead inhibitors (iKeap1) engages KEAP1 with nanomolar affinity (dissociation constant (Kd) = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify molecules that bind with high affinity to target proteins.

Suggested Citation

  • Christoph Gorgulla & Andras Boeszoermenyi & Zi-Fu Wang & Patrick D. Fischer & Paul W. Coote & Krishna M. Padmanabha Das & Yehor S. Malets & Dmytro S. Radchenko & Yurii S. Moroz & David A. Scott & Kons, 2020. "An open-source drug discovery platform enables ultra-large virtual screens," Nature, Nature, vol. 580(7805), pages 663-668, April.
  • Handle: RePEc:nat:nature:v:580:y:2020:i:7805:d:10.1038_s41586-020-2117-z
    DOI: 10.1038/s41586-020-2117-z
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    Citations

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    Cited by:

    1. Wai Cheung Chan & Xiaoxi Liu & Robert S. Magin & Nicholas M. Girardi & Scott B. Ficarro & Wanyi Hu & Maria I. Tarazona Guzman & Cara A. Starnbach & Alejandra Felix & Guillaume Adelmant & Anthony C. Va, 2023. "Accelerating inhibitor discovery for deubiquitinating enzymes," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    2. Paul Beroza & James J. Crawford & Oleg Ganichkin & Leo Gendelev & Seth F. Harris & Raphael Klein & Anh Miu & Stefan Steinbacher & Franca-Maria Klingler & Christian Lemmen, 2022. "Chemical space docking enables large-scale structure-based virtual screening to discover ROCK1 kinase inhibitors," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Kun Wang & Chia-Wei Lee & Xuewu Sui & Siyoung Kim & Shuhui Wang & Aidan B. Higgs & Aaron J. Baublis & Gregory A. Voth & Maofu Liao & Tobias C. Walther & Robert V. Farese, 2023. "The structure of phosphatidylinositol remodeling MBOAT7 reveals its catalytic mechanism and enables inhibitor identification," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    4. Ayan Chatterjee & Robin Walters & Zohair Shafi & Omair Shafi Ahmed & Michael Sebek & Deisy Gysi & Rose Yu & Tina Eliassi-Rad & Albert-László Barabási & Giulia Menichetti, 2023. "Improving the generalizability of protein-ligand binding predictions with AI-Bind," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    5. Lifan Chen & Zisheng Fan & Jie Chang & Ruirui Yang & Hui Hou & Hao Guo & Yinghui Zhang & Tianbiao Yang & Chenmao Zhou & Qibang Sui & Zhengyang Chen & Chen Zheng & Xinyue Hao & Keke Zhang & Rongrong Cu, 2023. "Sequence-based drug design as a concept in computational drug design," Nature Communications, Nature, vol. 14(1), pages 1-21, December.
    6. Yi An & Jiwoong Lim & Marta Glavatskikh & Xiaowen Wang & Jacqueline Norris-Drouin & P. Brian Hardy & Tina M. Leisner & Kenneth H. Pearce & Dmitri Kireev, 2024. "In silico fragment-based discovery of CIB1-directed anti-tumor agents by FRASE-bot," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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