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11 Ultra-Large-Scale Virtual Screening 335
Fig. 11.6 Summary of all discussed ultra-large VS campaigns. Studies are grouped by year and
color coded by screening approach. The y-axis indicates library sizes. In the inset on the right-hand
side, sizes of all discussed publicly available chemical libraries and spaces are displayed for
reference. Gray circles mark enumerated, white circles—nonenumerated libraries
reported hit rates (compare Table 11.8; typical hit finding campaigns achieve only
about 1% hit rate according to the literature [89]) should be taken with a grain of salt
and, given the human factor involved, not directly translated into an argument for
pursuing ultra-large-scale screening. Owing to this notion, the debate whether bigger
is better remains ongoing. Arguments can be made both in favor and against ultralarge VS [98]. We are currently only beginning to scratch the surface of ultra-largescale VS, and it is, in our and others’ opinion, premature to draw a conclusion
whether—and, importantly, when—bigger is actually better [1, 98, 99].
Acknowledgements I.P., T.S., and A.P. gratefully acknowledge the support of the Jane and Aatos
Erkko Foundation. We thank Laxman Yetukuri of CSC for his critical review and helpful feedback
on our discussion of HPC systems.
336 I. Pöhner et al.
Appendix
Table 11.9 List of URLs for discussed enumerated ultra-large screening libraries
Database Available from
Enamine REAL database https://enamine.net/compound-collections/real-com
Enamine REAL lead-like, natural
product-like compounds
GalaXi enumerated https://www.labnetwork.com/frontendapp/p/#!/
SAVI https://doi.org/10.35115/37n9-5738
ZINC22 https://cartblanche22.docking.org/
ZINC20 https://zinc20.docking.org/
ZINC15 https://zinc15.docking.org/
CHIPMUNK http://www.ewit.ccb.tu-dortmund.de/ag-koch/
An online version of this Table is also available from https://github.com/ipohner/ultralarge-VS,
where references and table contents will be periodically updated
Table 11.10 Summary of discussed open-source VS tools and their availability
Tool Reference(s) Available from
DeepDocking Gentile et al.
DeepDocking GUI Yaacoub
HASTEN Kalliokoski
Lean docking Berenger
Linear accelerated
docking
MolPAL Graff et al.
Schrödinger GPU similarity (incentive)
SpaceDock Sindt et al.
Thompson sampling Klarich et al.
V-SYNTHES Sadybekov
VirtualFlow Gorgulla
warpDOCK McDougal
An online version of this Table is also available from https://github.com/ipohner/ultralarge-VS,
where references and table contents will be periodically updated
[71]
et al. [76]
[79]
et al. [84]
Marin et al.
[85]
[77]
[90]
[35]
et al. [22]
et al. [7]
et al. [67]
pounds/real-database
https://enamine.net/compound-collections/real-com
pounds/real-database-subsets
library/virtual
https://cactus.nci.nih.gov/download/savi_download/
chipmunk/
https://github.com/jamesgleave/DD_protocol
https://github.com/jamesgleave/DeepDockingGUI
https://github.com/TuomoKalliokoski/HASTEN
Encoder: https://github.com/UnixJunkie/molenc Support Vector regressor: https://github.com/UnixJunkie/
linwrap
https://github.com/marinegor/Linear-accelerated-
docking
https://github.com/coleygroup/molpal
https://github.com/schrodinger/gpusimilarity
https://github.com/litfsindt/LIT-SpaceDock
https://github.com/PatWalters/TS
https://github.com/katritchlab/V-SYNTHES
https://github.com/VirtualFlow
https://github.com/BruningLab/warpDOCK
11 Ultra-Large-Scale Virtual Screening 337
Table 11.11 Summary of available screening libraries with pre-generated 3D conformers and
benchmarking datasets
Pre-generated 3D screening libraries — Description and reference
→ available from
Enamine REAL 1.4 billion compounds in PDBQT format by Gorgulla et al. [7]
→ via https://virtual-flow.org/real-library with login
ZINC15 1.5 billion compounds by Gorgulla et al. [5]
→ via https://virtual-flow.org/virtualflow-version-zinc15-library with login
Enamine REAL lead-like (2021) 3D conformers from 1.56 billion SMILES input in Schrödinger
Phase databases by Sivula et al. [23]
→ https://doi.org/10.23729/2de314bb-59af-452a-955c-c2ff0c5ea57f
Pre-generated 3D conformer tranches of ZINC20 as referenced by Bender et al. [24]
→ https://files.docking.org/3D/
Ultra-large benchmarking datasets — Description and reference
→ available from
1.56 billion SMILES + Glide-HTVS docking scores for SurA and GAK (csv) by Sivula et al. [23]
→ https://doi.org/10.23729/2170dc9c-4905-43c3-aeee-a574d360737f
1.4 billion SMILES + AutoDock-GPU docking scores and re-scoring results for 5 SARS-CoV-2
protein targets (parquet) as described in Rogers et al. [56]
→ https://doi.org/10.13139/OLCF/1783186
138 million SMILES + DOCK docking scores for dopamin D
receptor by Lyu et al. [17]
4
→ https://doi.org/10.6084/m9.figshare.7359401.v3
99 million SMILES + DOCK docking scores for AmpC by Lyu et al. [17]
→ https://doi.org/10.6084/m9.figshare.7359626.v2
138 million D
+ 99 million AmpC Glide docking scores by Yang et al. [81]
4
→ https://s3.amazonaws.com/content.schrodinger.com/Resources/paper_data_share.zip
All URLs reported below were last checked January 31st, 2024. An online version of this Table is
also available from https://github.com/ipohner/ultralarge-VS, where references and table contents
will be periodically updated
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Part II
The Pitfalls Between Experimentation
and Simulation