WHITE PAPER

Dramatically
improving hit rates
with a modern virtual
screening workflow
Scientists from Schrödinger’s Therapeutics Group leveraged a modern
virtual screening workflow powered by ultra-large scale docking andabsolute binding free energy calculations to achieve a double-digit hit
rate for diverse protein targets.

Executive Summary• Schrödinger developed a modern virtual screening workflow forsmall molecule ligands and fragments that enabled Schrödinger’s
Therapeutics Group to repeatedly achieve unprecedented success in
its hit discovery efforts• The modern virtual screening workflow efficiently screens ultralarge libraries of up to several billion purchasable compounds withunrivaled accuracy, through machine learning enhanced docking
and absolute binding free energy calculation technologies• The workflow was successfully applied to a broad range oftargets across multiple screening campaigns, for both wholeligands and fragments• Schrödinger’s Therapeutics Group used the workflow to identify
multiple experimentally confirmed hits with diverse chemotypes
while dramatically narrowing down the number of compounds
made or purchased and assayed in the lab — frequently achieving
double-digit hit rates

BackgroundFor years, hit discovery efforts using traditional
virtual screening (VS) approaches have sufferedfrom low hit rates, typically 1-2% in Schrödinger’s
experience, which means that 100 compounds
would have to be synthesized and assayed for
1-2 hits to be identified. These challenges havelargely been attributed to two key factors:

Second, traditional VS methods have been
limited by the inaccuracy of the scoring methods
utilized to rank order different ligands, such as
GlideScore. Given a static view of the complex
geometry and an approximate treatment ofdesolvation, such empirical scoring functions
aren’t theoretically suited to quantitatively rankcompounds by affinity. Thus, while docking is a
powerful technology for early enrichment, ligand
docking scores are not expected to and generally
do not correlate with measured potency.

First, traditional VS campaigns have been limited
to libraries in the hundreds of thousands to afew million in size, providing limited coverageof chemical space. This is particularly critical for
difficult-to-drug targets, where the random hit
rate in the library is expected to be low, hence
fewer hits are expected to be recovered withsmaller libraries. In recent years, the emergenceof ultra-large commercial chemical libraries suchas Enamine REAL and research demonstrating
the value of screening large libraries has further
driven the need for technologies that canefficiently screen ultra-large chemical space.1

As a result of these limitations, most resources
spent on virtual screens using traditional methods
are often wasted. A more cost-effective and efficientapproach to accurately screen ultra-large libraries isrequired to improve the success of virtual screensand ensure it is a viable path for hit discovery.

Commercial or Fragment Library
Pre-ﬁltering

Screening(Ultra large scale)>5B compounds>600M fragments

Physico-chemical properties, undesired group, etc…

Active Learning Glide+ GPU Shape~5B (x2)

100,000s

WScoreRescoring(Accurate)

AB-FEP~1,500Large Scale
Rescoring(Active Learning AB-FEP)

Post-ﬁltering

Purchase List

10s-100s cmpds

Figure 1: Overview of Schrödinger’s modern virtual screening workflow.

2Design Approach
Over several years, Schrödinger’s Therapeutics
Group has selected a number of challenging
targets with the goal of identifying potent hitmolecules. The group turned to a modern VS
workflow, leveraging machine learning-guided
Glide docking and highly accurate Absolute
Binding FEP+ (ABFEP+) calculations, to screen
and rescore ultra-large chemical libraries ina way that minimizes wet-lab costs and time
while increasing the number and quality of hits
available for hit-to-lead progression (Figure 1).

Step 1: Ultra-large scale screening
for small molecule libraries
Starting with libraries on the order of several billion
compounds (or libraries of up to 500 million forfragments), the team performed prefiltering basedon physicochemical properties to eliminate any
undesired groups. Next, they carried out a highthroughput virtual screen with Active Learning
Glide (AL-Glide), in order to quickly identify themost promising compounds. Active learning is aneffective supervised learning strategy that prioritizes
training data for the next round of training based ona well-defined objective. AL-Glide combines machinelearning (ML) with docking so that enrichment withdocking can be applied to libraries of billions ofcompounds.2 By using this approach, only a fractionof the library is docked, reducing the computational
cost significantly to a more reasonable level.
At the start of an active learning cycle, a manageable
batch of compounds is selected from the complete
data set of library compounds and docked. These
selected compounds are then taken and added tothe training set. The model is then trained on new
information and continues to iterate this process
as the machine learning model becomes a betterand better proxy for the docking method (Figure 2).
This ML-guided docking model can evaluate
compounds much more quickly than brute
force docking. While the typical docking
calculation with Glide might take an averageof a few seconds per compound, the MLmodel can evaluate or make a prediction
significantly faster, leading to a drastic increasein throughput. As a result, the ML-generated
model is used to evaluate the entire library.
After completion of the AL-Glide screen, theteam performed a full docking calculation using
Glide on the best scored compounds, typicallyin the range of 10-100 million compounds.

Score complete dataset by ML model
Complete
datasetSelectN ligands

Physics-based
scoring

Used for:ML Training on
physics-based
scoresPhysics-based rescoring
using longer protocol(FEP+) or more of the top
ligands (Glide)

Hitlist• Active Learning Glide (AL-Glide)• Active Learning FEP+ (AL-FEP)• Active Learning Absolute Binding FEP+ (AL-ABFEP)

Figure 2: Schematic showing an active learning workflow for hit discovery.

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Applying the modern
virtual screening
workflow to fragments

Step 2: RescoringThe most promising compounds based on Glide
docking scores were then selected and subjected
to a rescoring step using WScore, a sophisticateddocking program that leverages explicit water
information in the binding site to enrich active
molecules over Glide alone, in large part due toimproved pose prediction. WScore helps identify
better compounds to pass to ABFEP+, the next
scoring step, and to provide more reliable binding
poses, reducing false positives in the process.3

Experimental fragment screening has led
to multiple FDA-approved drugs and clinicalcandidates. By adapting this modern workflow to
fragment screening, Schrödinger’s Therapeutics
Group has successfully scaled up screening tomillions of fragments, as compared to 3k to
30k fragments screened by traditional HTS.
The in silico approach addresses a fundamental
limitation of experimental fragment screening— the fragments need to be soluble enough to
be assayed at high concentrations (100 μM tomM) against various targets. However, estimating
the potency rigorously in silico is not limited bysolubility, so the potency of the fragments canbe assessed and subsequently pursued if they
are predicted to be soluble enough given theirestimated potency. The binding potency to thespecific target is computed using active learning
ABFEP+. Priority fragments are finally evaluatedfor solubility in silico at predicted potency using
Solubility FEP+.6 In essence, the approach achieves
scale by inverting the problem of potency andsolubility, enabling the discovery of highly potent
and ligand efficient fragments that would notexist in experimental fragment libraries.

Compounds with the best enrichment scoresfrom docking are selected for rigorousrescoring with ABFEP+. ABFEP+ is a protocol in
Schrödinger’s FEP+ technology that allows theaccurate calculation of binding free energies
between the bound and unbound states of theligand/protein complex (Figure 3).4,5 ABFEP+ has
proven to reliably correlate with experimentallymeasured binding affinities. Unlike relative
binding FEP+, ABFEP+ does not require a similar,
experimentally measured reference compound
as a starting point. Because ABFEP+ can evaluate
and accurately score diverse chemotypes, it is alinchpin technology to discover the most potentcompounds in a virtual screen campaign.

Step 3: Large-Scale Rescoring
ABFEP+ is computationally expensive whencompared to Relative Binding FEP+ (RB-FEP+),
requiring multiple GPUs per ligand andapproximately 4x more compute time. It is
generally only practical to run thousands of ABFEP+
calculations on a hit discovery campaign. In orderto realize the true enrichment benefit of ABFEP+, an
active learning approach is utilized to score a muchlarger number of compounds.

ΔGbinding

Cross-link restraints

Cross-linkrestraints

To date, the team has completed a total of ninelarge fragment-based* virtual screens on multiple
challenging targets, including one with a homology
model. All nine screens yielded multiple potent
ligand efficient hits, ranging from low nM to30 µM in potency and double-digit hit rates.

•Solvent leg (top row): Alchemical transformation froma physical to a dummy ligand in solvent•

Complex leg (bottom row): Alchemical transformation froma physical to a dummy ligand cross-linked with protein•

Free energy of cross-link restraints in the dummy
ligand state is calculated analyticallyFigure 3: Thermodynamic cycle for Absolute Binding FEP+(ABFEP+). As part of the modern virtual screening workflow
on discovery programs, the accuracy provided by the rigorous
calculations in ABFEP+ consistently showed very early
enrichment in top actives and reduced false positives, which is
critical when screening ultra-large libraries.

* Compounds with molecular masses between 100 and 250 Da

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Impact of Schrödinger’s
modern VS workflow on
hit rates across multiple
projects and targets
Using these modern VS approaches, scientists
at Schrödinger were able to demonstrate adrastic improvement in hit rates compared
to traditional screens. As a result, several
diverse hit compounds with high predictedbinding affinity were identified, acquired,experimentally tested and confirmed ashits — resulting in an impressive doubledigit percentage hit rate (Figure 4).

Traditional
Virtual Screen

ModernVirtual Screen

Sample of Screensin Schrödinger’s
Therapeutics Group

814

Average Hit Rate

1%

26%

Screens with Hit
Rate > 10%

09Screens Where Hits
Were Progressed07

Technology StackDocking

Docking, Machine
Learning, ABFEP

Figure 4: Impact of transitioning from traditional to modern virtual
screening on hit rate.

Conclusion
In silico hit identification has long relied on smaller scale libraries
and lower accuracy methods to screen the chemical space,resulting in low hit rates and largely wasted wet lab resources.
By transitioning to a modern workflow that leverages rigorous
physics-based methods, including absolute binding FEP+ combinedwith machine learning, Schrödinger’s Therapeutics Group has
been able to successfully apply the workflow to a range of diverse
targets across several projects and achieve a reproducible doubledigit hit rate. In the process, the team dramatically reduced thenumber of compounds synthesized and tested to reach the project’s
lead candidate, reducing overall costs and project timelines.
This strategy empowers drug discovery teams by enabling them toexplore the fast-growing ultra-large chemical libraries. It allows efficientnavigation through the vast maze of chemical space, significantly
improving the odds of identifying multiple hits with better properties
and selectivity. Moreover, it accelerates the drug development process,leading to the faster discovery of higher-quality, novel drug candidates.

5Enabling digital technologies to drive discovery programs
FEP+GlideWScore

Digital assay for predicting
protein-ligand binding
across broad chemical
space at an accuracy
matching experimental
methods

Leading industrial solution
for easy-to-use, reliableligand-receptor docking

Advanced docking
program that leverages
explicit water informationin the binding site toprovide more accurate
scoring of ligands

Active Learning
Glide
Powerful machinelearning (ML) tool thattrains ML to efficiently
prioritize and selectcompounds forexperimental evaluationor further screening

References
1.

Ultra-large library docking for discovering new chemotypes. Lyu J, et al. Nature. 2019 Feb; 566(7743): 224–229.

2.

Efficient exploration of chemical space with docking and deep learning. Yang Y, te al. J. Chem. Theory Comput.
2021, 17, 11, 7106–7119.

3.

WScore: A flexible and accurate treatment of explicit water molecules in ligand−receptor docking. Murphy RB,et al. J. Med. Chem. 2016, 59, 4364−4384.

4.

Enhancing hit discovery in virtual screening through accurate calculation of absolute protein-ligand binding
free energies. Chen W, et al. J. Chem. Inf. Model. 2023, 63, 10, 3171–3185.

5.

Accurate calculation of the absolute free energy of binding for drug molecules. Aldeghi M, et al. Chem. Sci.
2016;7:207–218.

6.

Novel physics-based ensemble modeling approach that utilizes 3D molecular conformation and packing toaccess aqueous thermodynamic solubility: A case study of orally available bromodomain and extraterminal
domain inhibitor lead optimization series. Hong RS, et al. J. Chem. Inf. Model. 2021, 61, 3, 1412–1426.

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