CASE STUDYHigh precision,
computationally-guided
discovery of highly selective
Wee1 inhibitors for thetreatment of solid tumors
Design approach leverages rigorous physics-based modeling to overcome
off-target liabilities en route to development candidate SGR-3515

Performed rapid in silico design
cycles using a collaborative
platform and a large-scale
de novo design workflow

Optimized potency andselectivity with relative binding
FEP+ and protein FEP+

“This program demonstrates the firstprospective application of protein FEP+
to model broad kinome selectivity,
which not only enabled the Wee1 teamto rapidly identify a few highly selective
chemotypes and rescue a program that
had hit a major roadblock, but it also
added a powerful tool for the broader
drug discovery community to tackle
selectivity challenges more efficiently.”— Jiashi Wang
Senior Director, Medicinal Chemistry
Schrödinger Therapeutics Group

Resulted in a development
candidate currently inpreclinical development

TargetWee1, Ser/Thr kinase

ProgramType

Schrödinger proprietary
program, small molecule

Indication

Solid tumorsStage

IND-enabling studies

DESIGN CHALLENGE
Inhibition of Wee1, a serine/
threonine protein kinase which
serves as the gatekeeper of theG2-M cell-cycle checkpoint, forces
cells into unscheduled mitosis andculminates in cell death. Given its
critical role in DNA repair, Wee1 is
an attractive target for oncology
drug development. Clinical trial data
for the Wee1 inhibitor adavosertib(AZD-1775) validated the potential oftargeting Wee1 by revealing strong
anti-cancer activity in solid tumors.1-11
However, it was suspected that AZD1775 had potential liabilities due toinhibition of several other kinases,including the PLK family, as well astime-dependent inhibition of CYP3A4.
The aim of this program, driven by
Schrödinger Therapeutics Group,
was to develop a best-in-class, highly
selective Wee1 inhibitor by leveraging
rigorous physics-based modelingapproaches to address off-target
liabilities with high precision.

Enumerations andhand-drawn ideas

FEP+
Wee1 (On target)

FEP+
PLK1 (Oﬀ target)

Team selections
and synthesis

Efficient generation of a selective andpotent lead series with a rigorous FEPbased workflow
At project onset, the team’s goal was to identify one or multiple novel leadseries with improved selectivity for Wee1 as compared to polo-like kinase
1 (PLK1). The team enumerated over six million ideas and hand-drawn
designs, then triaged them using an automated chemistry-based filtering
workflow, structure-based screening (docking), and ultimately rigorous
calculation of relative binding free energies via free energy perturbations(FEP+, De Novo Design Workflow) (Figure 1). The accuracy and utilityof FEP+ as a computational binding affinity assay has been validated
extensively, generating predictions within 1.0 kcal/mol of experimental
values on average.12 By combining FEP+ with high performance cloudcomputing and machine learning (Active Learning FEP+), over 9,000 FEP+
calculations were performed to evaluate the affinity of the most promising
designs for inhibiting Wee1 and ensuring selectivity over PLK1.
Design ideas were then evaluated by the project team in LiveDesign,
Schrödinger’s cloud-based enterprise informatics platform. These
workflows informed the selection of less than 100 compounds forsynthesis and testing, resulting in the identification of multiple novel
chemotypes with nanomolar affinity and enhanced selectivity (up to1000x) for Wee1 over PLK1 within seven months from project start.13

6,700,000 ideas

in 7 months
9,051
FEP+
calculations

80 compounds

5 NovelChemical Series
Wee1 IC50: 5-13 nM
PLK1 selectivity: 30-1000x

Figure 1: FEP+ workflow to identify potent and selective Wee1
inhibitors, and enhance selectivity for Wee1 over PLK1.

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Addressing additional off-target
liabilities with protein FEP+
Although the team successfully improved selectivity
against PLK1 in the first phase of the project, subsequentkinase panel screening revealed a significant numberof unanticipated off-target kinase liabilities. In lieu ofstarting over or scrapping the program entirely, the team
explored a complementary strategy to improve selectivityof the chemotypes identified in the first phase. A major
driver of selectivity seemed to be a specific residue in thebinding site. The team utilized protein FEP+ calculations,a protocol within FEP+, to assess the impact of single
point-mutations at that location on the ligand affinity
and to infer selectivity across a large diversity of kinases
without the need to profile each kinase separately. In
this new workflow, 6,700 new designs were profiled withligand FEP+ for predicting potency against Wee1 andwith protein FEP+ for predicting broad kinome selectivity.
During this three month modeling campaign, the ligand
FEP+ and protein FEP+ strategy identified 42 promising
molecules for synthesis and 22 of these molecules
exhibited low nanomolar to picomolar measured
potencies against Wee1 with substantially reduced
selectivity liabilities (Figure 2).14
One exquisitely selective molecule, STC-8123, wasused as a proof-of-concept compound to demonstratethat highly selective Wee1 inhibitors retain profoundin vivo efficacy. Indeed, in an A427 mouse model withintermittent dosing, STC-8123 demonstrated rapid andmore sustainable inhibition of tumor growth comparedwith AZD-1775 (Figure 3).14
Original Hit(Series 1)

Compound 2(Series 1)

Compound 3(Series 2)

STC-8123(Series 3)

Tumor size (mm3)Figure 2: Kinome selectivity profiling using scanMAX Kinase Assay Panel confirms thatthe FEP+/Protein FEP+ workflow enabled the rapid optimization of non-selective hits into
multiple series with gene-family wide selectivity — as exemplified by STC-8123.

Optimizing DMPK and ADME
properties with machinelearning and quantum
mechanics strategies
With a promising chemical series in hand, the team began
the process of optimizing DMPK and ADME properties.
As experimental data accumulated for the lead series,
the team trained project-specific machine learning(ML) models to predict time-dependent inhibition of
CYP3A4 profiles as well as a variety of ADME properties(DeepAutoQSAR). In addition, quantum mechanical (QM)
calculations were developed for modeling compoundreactivity with the CYP3A4 heme, a potential target for
drug-drug interaction liabilities (Jaguar). During late-stage
lead optimization, this breadth of ML models and QM
physics-based calculations enabled prospective multiparameter optimization to readily narrow down thechemical space that was sent for synthesis and testing.
The team identified several advanced leads fromamong the ~300 compounds synthesized in the series.
Upon in-depth profiling of advanced leads and carefulconsideration of the properties that a best-in-class, next
generation Wee1 inhibitor should have, SGR-3515 wasnominated as the development candidate. SGR-3515 is an
exquisitely selective, potent molecule that is structurallydifferentiated from competitors’ molecules, has adifferentiated ADME profile, and achieves superior in vivo
efficacy (Figure 4).

AZD-1775

Zn-C3

SGR-3515

Wee1 SPR KD (nM)

1.4

0.9

<0.1

Cell target engagement (pCDC2) IC50(nM) in A427 andOVCAR3 cell lines

130 / 80

260 / 14060 / 85

Cell viability IC50 (nM)
CTG assay in A427 andOVCAR3 cell lines

190 / 280

200 / 210

80 / 100

Weak CYP3A4inhibition
CYP3A4 TDI

Weak CYP3A4inhibition
CYP3A4 TDI

No reversible
CYP inhibition
ReducedCYP3A4 TDI

Kinome selectivity
scanMAX (1uM for
AZD-1775, Zn-C3;
500 nM for SGR-3515),
403 kinases tested

Drug-drug Interaction
liability

Stop Dosing

Dosing day
Figure 3: No tumor regrowth observed after STC-8123 high dose treatment stopped inA427 (non-small cell lung adenocarcinoma) tumor model.14

Figure 4: Comparison of SGR-3515 with competitors’ Wee1 inhibitors.15 SGR-3515 shows
superior binding affinity and potency in cellular assays in A427 and OVCAR3 (high-grade
serous ovarian adenocarcinoma) cell lines, demonstrates excellent broad kinome selectivity,
and reduces drug-drug interaction potential due to decreased time-dependent inhibition (TDI)of CYP3A4. All competitor data was internally generated by contract research organizations.

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Enabling digital technologies to drive discovery programs
FEP+Elucidation of ligand
binding preferences
for large families
of off-targets withprotein FEP+ andon-target potency
with FEP+De Novo
DesignWorkflow
Ultra-large scalechemical space
exploration
combining multiple
compoundenumeration
strategies with anadvanced filtering
cascadeDeepAutoQSAR

Jaguar

LiveDesign

Scalable, intuitive
tool for training
and running
ML/AI models
automatically using
molecular inputs

High performancequantummechanics package
enabling compoundreactivity predictions

Collaborative
enterprise
informatics platform
for centralizing
access to virtualand wet lab project
data and powerfulcomputational
predictions

Software and services to meet your organization’s needs
Industry-Leading Software Platform
Deploy digital drug discovery workflows using a comprehensive and user-friendly platform for molecularmodeling, design, and collaboration.

Research Enablement Services
Leverage Schrödinger’s team of expert computational scientists to advance your projects through key
stages in the drug discovery process.

Scientific and Technical Support
Access expert support, educational materials, and training resources designed for both novice andexperienced users.

References
1-11.

Clin Cancer Res 2021: 27(4). 2. J Clin. Oncol 2021: 39(14):1531-1539. 3. J Clin. Oncol 40, no. 16_suppl (June 01, 2022)
5515. 4. Lancet 2021: 398:281-92. 5. J Clin. Oncol 2016: 34:4354-4361. 6. Clin Cancer Res 2018: 24(120): 2740-8. 7.
Clin Cancer Res 2018: 15;24(12):2740-2748. 8. Zentalis investor deck. 9. Annals of Oncology 2020: 31 (suppl_4). 10. J
Clin Oncol 2019 Oct 10;37(29). 11. Cancer Res (2019) 79 (13_Supplement): CT02. 12.J Clin Oncol 2021 Nov 20;39(33).

12.

Advancing drug discovery through enhanced free energy calculations. Abel et al. Acc. Chem. Res. 2017, 50, 7,1625–1632.

13.

De-risking off-target liabilities with protein free energy methods. Knight et al. ACS 2022.

14.

Discovery of potent, selective, and orally available WEE1 inhibitors that demonstrate increased DNA damage andmitosis in tumor cells leading to tumor regression in vivo. Sun et al. AACR 2022.

15.

Into the clinic: Transforming the drug discovery process with digital chemistry. Davis et al. Lab of the Future 2023.

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