WHITE PAPER

QuickShapescreening in
the age ofultra-large
libraries
Tim Knehans, Steve Dixon, Nick Boyles,
Jianxin Duan, Chris von Bargen, Volodymyr
Babin, Steve Jerome, Matt Repasky

Readily available chemical libraries continue toincrease rapidly in size while the need for novel
chemical matter for drug targets remains at an alltime high. Although 3-dimensional Shape-based
methodologies provide an effective approach toenrich actives by finding hits that are topologicallydifferent but similar in shape, their hardware
requirements can become prohibitive forscreening ultra-large chemical libraries. Here wecompare the performance of a 1-dimensional (1D)
pharmacophore-based fingerprint with GPU-based
Shape Screening both in terms of enrichment ofknown actives as well as computational speed.
The 1D method not only retains enrichment atlevels very similar to Shape Screening with highoverlap of recovered actives but also provides
distinct hits. Additionally, it allows screening ofbillions of molecules on a conventional laptop withina few days and requires 100 times less storage.
By combining both 1D and 3D approaches, wedeveloped an automated staged workflow called
QuickShape enabling efficient 3D-Shape screenings
for library sizes in the tens of billions of molecules.
This workflow is an excellent ligand-based screening
solution in the new era of ultra-large libraries.

1D-fingerprint technology enables
pre-screening of Enamine REAL in a few
hours within QuickShape workflow
QuickShape is a cascaded screening workflow using 1D pharmacophoric fingerprint
screening followed by Shape Screening. Pharmacophores are perceived for each moleculewith appropriate protonation and tautomer states and subsequently projected into afingerprint vector. The distance between the pharmacophoric points is the number of bonds
between the pharmacophores.
This process takes approximately 1 CPU hour per 1 million molecules. The output databaserequires 100 times less storage compared to equivalent Shape GPU databases. A 6.5 billion
compound database takes up only 400 GB.
The similarity is defined by comparing the overlap of equivalent pharmacophores between
the molecules A and B:
Sim(A, B) = Overlap(A, B)/max[Overlap(A, A), Overlap(B, B)]
The similarity comparison is extremely fast and parallelizable. It is possible to screen 6.5
billion compounds on a computer with 10 cores in 14 hours.
The hits from 1D screening are triaged using Shape Screening and the final hit list can beanalyzed based on diversity, specific pharmacophore criteria and property filters using theHit Analyzer panel in Maestro. The complete workflow can be run on a laptop in a few days.

Position

Input molecules

1D database generation

1D pre-screen
LigPrep + ConfGen

Hit analyzer

Shape CPU2

1D Screening provides
similar enrichment
compared toShape screensFirst we validated the enrichment of 1D similarity
against that of Shape using the actives from theDUD-E dataset seeded in 1.4 billion moleculesfrom Enamine REAL. The five most diverse actives
were used as probes. Both methods provide very
similar enrichment of known actives. Among top
200,000 hits, actives were identified for all targets,indicating 1D screening alone can be useful. It is
important to note that real enrichment may bemuch higher because there are likely additional
unknown actives in the Enamine REAL database.

1D Screening & Shapehave good correlationand high overlap
Secondly, we ensure that 1D screening is able toretrieve the same molecules as Shape, which isrepresented by the percent overlap among thetop hits. Among the top 20 million hits from 1D
screening we find a minimum 50% and on average
90% of the top 200,000 hits from Shape screening.
Already at 200,000 returned hits, the averageoverlap is >60% indicating that it may be sufficient
to run 100 times less Shape calculations.
The 1D similarity score is highly correlatedwith Shape similarity with an R2 of 0.65. Thisdemonstrates that 1D screening is able to reduce
the number of Shape comparisons by 84% while
maintaining similar performance.

3

QuickShape and its constituent technologies should be
used in a combined fashion
Since the enrichment between Shape and 1D screening(1DSIM) are similar but neither correlation nor overlap are100%, we visualized the trends in unique contribution tothe recovered actives by 1D Screening via calculating:#actives1DSIM - 2 x #actives1DSIM ∩ ShapeIn this representation, all values above 0 show targets
where at least twice as many actives uniquely returnedby 1D screening compared to overlapping hits.

Contact Us: sales@schrodinger.com

Copyright © 2023 Schrödinger, Inc.

As with previous figures the more molecules are returned
by each methodology the bigger the overlap gets andthe distributions are shifted below 0 which makes thesemolecules accessible to the QuickShape workflow.
However, in all bins, significant amounts of molecules
are above and close to 0. This indicates the ability of 1D
screening to contribute valuable hit matter on its own
hence it is generally recommended to investigate thetop-ranked molecules by either methodology in order toincrease the likelihood of novel hit identification.