CASE STUDY

Hit to Lead Design ofNovel D-amino-acidOxidase Inhibitors usinga Comprehensive Digital
Chemistry Strategy
Computational platform grounded in highly accurate predictivemodels enables team-based discovery of a novel chemical series
engaging a complex CNS target.

Overview
Inhibition of D-amino-acid oxidase (DAO) has been hypothesized as apotential therapeutic strategy for schizophrenia. Schrödinger’s Drug
Discovery Team engaged in a discovery effort with a collaborator toidentify novel DAO inhibitors with potential best-in-class properties.

Program Challenges• Identify novel chemical matter while striving for best-in-classmolecules that cross the blood-brain-barrier• Simultaneously optimize drug-like properties, improve CNS
exposure, and affinity

Approach
The Drug Discovery Team deployed a large-scale digital chemistrystrategy leveraging:• A centralized project data platform to facilitate knowledge-based
medicinal chemistry design collaboration (LiveDesign, AutoQSAR)• Physics-based methods to predict affinity and prioritize designideas for synthesis (FEP+)• Computationally-driven ideation and scoring workflow to amplify
common enumeration strategies and screen hundreds of millions ofcompounds using machine learning coupled with physics-based
free energy methods (FEP+, AutoDesigner)

Results
The team discovered a novel class of DAO inhibitors with desirable
drug-like properties by confidently exploring synthetically-challenging
chemistry. The team also identified a previously unexplored subpocket
for further evaluation. The novelty of the compounds, coupled withwell-balanced properties, demonstrates the extraordinary power of
the approach to unleash project team creativity. By leveraging a digitalplatform, the team explored vast chemical space while simultaneously
optimizing for drug-like properties in a challenging disease area.

2Why use a digital
chemistry approach?
A digital chemistry approach uses physics-based modelling, machinelearning, and a team-based collective intelligence platform to design
better molecules on accelerated timelines.

Enables
exploration ofchemical designs
with accuracy
approachingexperimental
methods

Optimized
through
Team-basedIntelligence

Grounded in
Physics

Facilitatescollaborative
design and allows
discovery teams
to test more
hypotheses

Amplified byMachine
Learning

Enhances
physics-basedmodeling
across vastchemical space3

How to achieve optimal
drug-like properties?
The development of CNS drugs poses several unique challenges. Fine-tuning
physicochemical properties for optimal brain exposure is an essential element ofCNS drug development. Many companies have discontinued neuroscience
discovery because these challenges lead to longer development timelines and alower probability of success.
Schrödinger’s Drug Discovery Group developed property prediction models, using
AutoQSAR deployed via LiveDesign, a web-based collaborative design platform.
LiveDesign enabled teams of medicinal chemists to crowdsource designs andsimultaneously optimize CNS properties with push-button workflows in a single
interface (see figure 1). Compounds predicted in the desired property space
were triaged using free energy perturbation (FEP+), a physics-based method for
accurately predicting compound binding affinity. This workflow empowers teams
to confidently pursue synthetically challenging compounds.

Figure 1. Schrödinger’s digital collaboration platform, LiveDesign, facilitates design optimization
through custom multiple parameter optimization models by centralizing program data and improving
team communication and collaboration.

4How did a digital chemistry strategy
enable improved hypothesis testing?
The team delivered high-quality molecules by working in an ecosystemthat facilitated exploration of vast, novel chemical space and simultaneous
optimization for desired properties through accurate physics-based modelingand machine learning.
The digital chemistry approach allowed the team to discover and quickly
overcome many medicinal chemistry challenges in the pursuit of best-in-classmolecules (see figure 1).2

DHP Dione Series

DHP Dione SeriesMethylated Cyclic DHP Dione Series

Good plasma PK with low Clearance
Decent brain penetration
Bioactive tautomer favored

Improved biochemical and cell potency
Desirable plasma PK with enhanced CNS exposure
Demonstrated PK/PD in vivo
Good in vivo tolerability

Water displacement to improve affinity
Desirable plasma PK
Low CNS exposure

Cmpd 1

Cmpd 3

Cmpd 5

Cmpd 2

Cmpd 4

Cmpd 6NHP Dione Series

Cyclic DHP Dione SeriesHigh Clearance
Hit class deprioritized

Improved affinity
Less improvement in cell assay

Extended DHP
Dione Series
Discovery of novel subpocketwith AutoDesigner
Low oral bioavailability
Low CNS exposure

Cmpd 1

Cmpd 2

Cmpd 3

Cmpd 4

Cmpd 5

Cmpd 6hDAO FEP+ predicted affinity (uM)

0.011

0.034

0.040

0.003

0.010

0.037

hDAO IC50 (uM)

0.34

0.028

0.092

0.025

0.009

0.008

hDAO Cell EC50 (uM)

3.1-0.60

0.29

0.14

0.14Figure 2. SAR progression to achieve key milestones through late lead optimization with key compounds
series represented. Crucial discovery and medicinal chemistry outcomes are highlighted. DHP
represents dihydropyrazine and NHP, N-hydroxyl pyrimidine.
5The team interrogated the atypical polarity of the DAO binding site,
shown in panel A of figure 3. Chemists pursued challenging chemistry
to reduce conformational flexibility and displace ahigh-energy water molecule by cyclizing and methylating cmpd 4 and 5(see figure 2, panels B and C).
Finally, while literature and crystallographic structures suggested
limited pocket volume for SAR exploration,3 FEP+ revealed theopportunity to interrogate this vector with larger chemical groups suchas cmpd 6, as shown in panel D of figure 3.

AB

Water

Cyclize linker

CDisplace waterDExplore novelsubpocket

Figure 3.
A) Polarity of DAO binding site required a polar warhead.
B) Cyclization of ligand linker reduced entropy improving affinity
C) Displacement of high energy water near the binding site improved affinity (compare water present in panel B with panel C).
D) FEP+ suggested exploring a novel subpocket predicted to improve potency (compare gray surface in panel A with the green surface in panel D).
6How to interrogate vastchemical space and delivernovel chemical matter?
Pursuing best-in-class molecules required exploring vast chemical spaceoutside of previously characterized drug-like molecules. The team utilized
AutoDesigner, a multifaceted large-scale enumeration workflow (figure 4),
to generate ideas exploring the novel DAO binding subpocket suggested by
FEP+ (see figure 3, panel D).4

Match molecular pair transforms

F3C
Novel Subpocket
Improve Affinity

AutoDesigner
Over 350 million uniquemolecules explored leading to14,676 novel subpocket designs

Reaction-based enumeration withPathFinder

Recursive trimming iteratively
breaking single bonds to increase
permutations

R-group decoration

Molecules prioritization using Active
Learning FEP+Filtering

Figure 4. AutoDesigner enumeration and triage workflow explores SAR from the lead molecule while
optimizing CNS drug-like properties to discover best-in-class molecules by covering vast chemical space.

To explore SAR and tune physicochemical properties, the teamperformed iterative cycles of AutoDesigner in the newly discovered
subpocket. After triaging with appropriate filters, all molecular ideas
were prioritized using free energy methods. The team trained activelearning models using physics-based affinity predictions (FEP+) toprioritize compounds for synthesis. In total, more than 350 million
ideas were generated and triaged.

7

What was the project impact?
Typically as drug discovery programs progress, teams struggle to balance
desired properties, which leads to deficits in desired drug properties as novel
scaffolds are explored and optimized.
Through a computational platform rooted in creative team collaboration,highly accurate predictive modeling, and enhanced by machine learning, apromising CNS DAO inhibitor series transitioned from hit discovery to lead
optimization with approximately 11,000 compounds scored by FEP+ and only
208 synthesized. Of the 208 compounds synthesized, only 20 were inactive(>10µM) against DAO. By discovering novel compounds and concurrently
performing multi-parameter optimization of critical CNS properties, the teamdelivered high project impact for this challenging disease area.
References
1. Bromet E.J., Fenning S.; Epidemiology and natural history of schizophrenia.
Biol Psychiatry. 1999, 46 (7), 871–881.
2. Tang et al. Discovery of a Novel Class of D-amino Acid Oxidase (DAO)
Inhibitors with the Schrödinger Computational Platform. ChemRxiv.
Preprint. https://doi.org/10.33774/chemrxiv-2021-dkf1k.
3. Hondo et. al. 4-Hydroxypyridazin-3(2H)-one Derivatives as Novel d-Amino
Acid Oxidase Inhibitors. J Med Chem. 2013, 56 (9): 3582-3592.
4. Bos et al. AutoDesigner, a De Novo Design Algorithm for Rapidly Exploring
Large Chemical Space for Lead Optimization: Application to the Design andSynthesis of D-Amino Acid Oxidase Inhibitors. ChemRxiv. Preprint.

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