Targeted and
Specialty Libraries

Coronavirus
More Than 15,000 Compounds Targeting SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), previously known as 2019 novel coronavirus (2019-nCoV), spread rapidly across the globe, creating an unparalleled global health burden. Developing drugs that target multiple points in the viral life cycle could serve as a strategy to tackle the current as well as future coronavirus pandemics.

The Coronavirus Library offers more than 15,000 compounds with potential to interact with SARS-CoV-2 viral targets or the host target ACE2. Compounds in the Coronavirus Library were selected in collaboration with the Wagner Lab at Harvard using the Virtual Flow methodology developed and published by the Wagner Lab. Details on the Virtual Flow methodology are published in Gorgulla, C., Boeszoermenyi, A., Wang, Z. et al. "An open-source drug discovery platform enables ultra-large virtual screens" Nature 580, 663–668 (2020). This SARS-CoV-2 / COVID-19 small molecule library may also have application in other coronavirus research programs.

View the Coronavirus Library product sheet.

Highlights
  • Generated in collaboration with a leading academic group
  • High quality, PAINS-free, lead-like and drug-like small molecule compounds
  • For SARS-CoV-2 / COVID-19 and other coronavirus research
  • More than 15,000 compounds available covering 17 targets
  • Purchase the full library or custom select a subset
Methodology

ChemBridge’s stock of more than 1.3 million compounds were prepared and docked against each of 40 different target sites on 17 different potential viral and host targets using the Virtual Flow screening platform. Top ranked compounds were further filtered to ensure that the Coronavirus Library included only high quality, lead-like or drug-like compounds free of undesirable chemical functionalities and free of PAINS structural alerts. The compound count per target ranges from 1,000 to 5,000 with 1,000 to 2,000 compounds per site screened.

Structures from ChemBridge stock were prepared with VirtualFlow for Ligand Preparation (VFLP)1. For each virtual screen a single target structure was used, and the protein was held rigid. QuickVina-W2 was used to perform a blind docking procedure for the HR1 domain of the spike protein, the RNA binding interface of the nucleoprotein, the RNA binding site of nsp12, as well as for nsp7 and ORF7a. For all the other site specific docking routines, QuickVina 23 was used. Both docking programs are based on AutoDock Vina4. The receptor structures were prepared with AutoDockTools5 from the PDB format to the PDBQT format.

Targets

Format
  • Custom select from more than 15,000 Coronavirus Library compounds or purchase all available compounds
  • The "Target" field in the structure file indicates the potential protein target(s)
  • Available in 96-well or 384-well format including acoustic compatible plates
  • Amounts as low as 0.25 micromole (25ul of 10mM DMSO solution) available
  • Compounds are available as DMSO solutions or dry in micromole or mg amounts

For more information or a file of compound structures, please contact ChemBridge Sales

References
  1. Gorgulla, C., Boeszoermenyi, A., Wang, Z. et al. An open-source drug discovery platform enables ultra-large virtual screens. Nature 580, 663–668 (2020).
  2. Protein-Ligand Blind Docking Using QuickVina-W With Inter-Process Spatio-Temporal Integration. Nafisa M. Hassan, Amr A. Alhossary, Yuguang Mu and Chee-Keong Kwoh. Nature Scientific Reports 7(1) (2017). DOI:10.1038/s41598-017-15571-7
  3. Fast, Accurate, and Reliable Molecular Docking with QuickVina 2. Amr Alhossary, Stephanus Daniel Handoko, Yuguang Mu, and Chee-Keong Kwoh. Bioinformatics (2015) 31 (13): 2214-2216. DOI:10.1093/bioinformatics/btv082
  4. O. Trott, A. J. Olson, AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading, Journal of Computational Chemistry 31 (2010) 455-461.
  5. Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S. and Olson, A. J. (2009) Autodock4 and AutoDockTools4: automated docking with selective receptor flexiblity. J. Computational Chemistry 2009, 16: 2785-91.