De Novo design tools for the generation of synthetically accessible ligands

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De Novo design tools for the generation of
synthetically accessible ligands
Peter Johnson, Krisztina Boda, Shane Weaver,
Aniko Valko, Vilmos Valko
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Receptor Structure Based Drug Design
Objective

• To suggest potential leads that
• bind strongly to a given protein because of shape
  and electrostatic complementarity
• Are easy to synthesise

Approaches

• Docking methods (preferably flexible docking)
  identify new lead structures by rapidly
  screening a database of 3-D structures of known
  compounds
• De novo design methods (such as SPROUT)
  construct a diverse set of entirely novel
  potential leads from scratch

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SPROUT Components
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Problem with Large Answer Sets
De novo design programs such as SPROUT can
suggest large sets of entirely novel potential
leads
Powerful heuristics are necessary to evaluate
(and reduce) often large answer sets
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For de novo design prediction of synthetic
accessibilty is equally important
Hypothetical ligands, including those predicted
to bind very strongly, have no practical value
unless they can be readily synthesised.
Our Attempts to Provide Solutions

• CAESA (estimates synthetic accessibility)
• Complexity Analysis (estimates structural
  complexity and drug-likeness)
• SynSPROUT (avoids the problem by building
  constraints into the structure generation process)

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CAESAComputer Assisted Estimation of Synthetic
Accessibility

• Glenn Myatt
• Jon Baber

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Goals of CAESA Project

• Clear need for automated method of ranking
  hypothetical compounds according to perceived
  ease of synthesis
• Good synthetic chemists can do this job
  themselves on small number of compounds but are
  unwilling to do it for hundreds or thousands of
  compounds
• CAESA attempts to do the same job but never gets
  bored!

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Estimation of Synthetic Accessibility Criteria
used by CAESA

• CAESA scores the synthetic accessibility of
  structures
• using two main criteria
• a) An estimate of structural complexity
• stereocentres
• complex topological features (fusions etc.)
• functional group complexity
• b) Availability of good starting materials
• rapid retrosynthetic analysis
• database of commercially available materials
• reaction rule base (editable)

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CAESA Components
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Automatic Selection of Starting Materials

• Starting Materials and Synthetic Accessibility
• Availability of suitable starting materials very
  important factor - good starting materials can
  dramatically reduce the difficulty of
  synthesising a compound.
• Good starting materials for part of the target
  molecule means the analysis of structural
  synthetic difficulty or complexity can be
  directed to just those portions of the target
  molecule that cannot be made from available
  starting materials
• Finding good starting materials through
  retrosynthetic analysis also provides possible
  synthetic routes as a byproduct

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Traditional Retrosynthetic Analysis
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Bidirectional Search for Synthetic Routes
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Example of Starting Material Selection
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Summary of CAESA Features

• CAESA carries out a retrosynthetic analysis which
  terminates when a starting material from a
  database (such as ACD) is found
• Found starting materials are scored according to
  length and difficulty of reaction sequence and
  coverage of target compound
• All chemistry rules and transformations are
  described in editable text knowledge bases easily
  modified by chemists
• Quality of the analysis depends on the chemistry
  included in the knowledge bases and the
  comprehensiveness of the starting material
  libraries
• But CAESA is relatively slow and speedier methods
  needed for pruning of large data sets

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Alternative ApproachComplexity Analysis
Based on statistical distribution of various
substitution patterns found in databases of
existing drugs and available starting
materials. Molecular Complexity Analysis of de
Novo Designed LigandsKrisztina Boda and A. Peter
JohnsonJ. Med. Chem. 2006 ASAP Web Release
Date 26-Jan-2006
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Assumption
If a molecular structure contains ring and chain
substitution patterns which are common in
existing drugs than the structure is likely to be
drug-like as well as readily synthesisable
available starting materials, then the structure
is likely to be readily synthesisable
Complexity analysis based on statistical
distribution of various substitution patterns
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Building Complexity Database
Enumerate chain patterns
Enumerate ring/ring substitution patterns

• 1-centred

• 2-centred

• 3-centred

• 4-centred

Database of chains
Database of rings/ring substitutions
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Atom Substitution Hierarchy
Ring (and chain) substitutions are organised in
hierarchies
The hierarchy stores

• Atom type sequence
• Number of occurrences
• Binding properties

Total occurrences of the topology 11,801
3591
1586
494
688
537
62
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Ligand Complexity Analysis
1. Enumerate ring and chain patterns
More Patterns
2. Generate canonical names for each atom pattern
Canonical name A
Canonical name B
Canonical name C
3. Match canonical name against the hierarchy
roots of the database
5. Rank structures by complexity score
Speed of Complexity Analysis 1000-1200
structures / minute on Linux PC (3GHz)
4. Retrieval of frequency of occurrences ?
Calculate score
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Calculation of Complexity Score
CONCEPT
Penalise atom patterns which
are infrequent or not present in the complexity
database.
Penalty values can be altered to tailor the
system for different applications.
In SPROUT the complexity analysis is followed by
ranking the putative ligands according to their
evaluated complexity score.
The penalty values used in the examples presented
here are 25, 20, 15, 10 for 1-,2-,3- and
4-centred chain patterns, 40 and 30 for rings and
ring substitutions.
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Validation ExperimentComparison with CAESA

• Both methods used to estimate synthetic
  accessibility for the same set of 50 top selling
  drugs

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CAESA vs. Complexity Analysis
Complexity scores are calculated using the
complexity database derived from available SMs
2.0 penalty for each identified stereo centre in
the structures.
Elapsed time CAESA 703 sec Complexity Analysis
8 sec
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Complexity Analysis vs CAESA

• More suitable for prioritization of thousands of
  structures within a reasonable time frame.
• Provides acceptable compromise between the speed
  of the analysis and the accuracy of calculated
  scores.
• Because this approach is based on characteristics
  of existing readily available compounds, simple
  but novel structural features may be wrongly
  identified as complex

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Yet another alternative approach Build synthetic
feasibility into the structure generation process


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SynSPROUT Approach
Classic SPROUT
SynSPROUT
Ease of synthesis is a key factor
in drug development
Build synthetic constraints
into structure generation process
fuse
Built in / user defined reactions Amide
formation Ether formation Ester formation Amine
alkylation Reductive amination etc.
spiro
new bond
SynSPROUT Scheme
VIRTUAL SYNTHESIS IN RECEPTOR CAVITY
Synthetic Knowledge Base
Fragment Library
Pool of readily available starting materials
Reliable high yielding reactions
Readily synthetisable putative ligand structures
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Current Status

• Promising structures with estimated high binding
  affinity
• SynSPROUT provides the equivalent to screening a
  large number of combinatorial libraries
• Potential for suggesting starting points for new
  combinatorial libraries
• Combination of a large starting material
  library with a large reaction knowledgebase
  causes a combinatorial problem even with
  parallel processing
• Restricting either size of library or number of
  synthetic reactions gives acceptable run times

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De Novo Structure Generation vs. Lead Optimization
De Novo Structure Generation
Lead Optimization
To suggest better ligands
structurally similar to the bound one
AIM
To generate diverse putative
ligands from scratch
AIM

No structural information from any existing bound
ligand is utilised
The structure of a good bound ligand provides a
starting point (core)

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Variations on the SynSPROUT ThemeSPROUT LeadOpt

• Two modes for structure based lead optimisation
• Core Extension Extends core structure (derived
  from lead) by virtual synthetic chemistry
• Monomer Replacement Replaces monomers which
  have been identified by retrosynthetic analysis
  of a lead compound

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Core Extension

• Import the modified bound ligand (core)
  identify substitution points (functional groups)
• Generate core monomer product by performing
  virtual synthetic reaction(s) at selected
  functional groups
• Estimate binding affinity for products

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Core Extension Scheme
Monomer Library
General Scheme
All possible core monomer combinations are
generated
Multiple low energy conformers detected
functional groups
Simulate synthetic reaction in the 3D context
of receptor site
Synthetic Knowledge Base
List of reactions (between functional groups)
Core Structure
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Automatic Monomer Library Generation
SDF file of 3D monomers
Perception Knowledge Base
Synthetic Knowledge Base
Atom Ring Perception

• Aromaticity
• Normalisation
• Hybridisation
• H-bonding
• properties

Functional Groups
Detect Functional Groups (joining points)
Synthetic rules
Monomer Library
Multiple low energy conformers detected
functional groups

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Synthetic Knowledge Base
CHEMICAL-LABEL ltCarboxylic Acidgt CSPCENTRE2(O)
-OHS1 CHEMICAL-LABEL ltPrimary
Aminegt C-NHS2CONNECTION1
EXPLANATION Amide Formation IF Carboxylic Acid
INTER Primary Amine THEN delete-atom 3
change-hybridization 5 to SP2 form-bond
- between 1 and 5 DIHEDRAL-ATOMS 2 1 5
4 DIHEDRAL 0 0 BOND-LENGTH 1.35 END-THEN
Steps of Joining Rules

• Steps of formation
• Hybridization changes
• Bond type
• Bond length
• Dihedral penalty/angle

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Importing the Core Structure (from MOL/PDB file
in Elephant module)
Importing from a pdb file pdb?mol converter is
invoked
Functional group(s) are automatically detected
when the core structure is imported into the
system
Hydrogen donor/acceptor or spheric target sites
anchor the imported core structure inside the
receptor cavity, partially restricting the
displacement of the core during lead
optimization, but allowing slight movements in
order to avoid boundary violations.

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Product Generation I.
Step I.
Generate products by mimicking synthetic
reactions between core monomers
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Product Generation II.
Step II.
Ligand flexibility generate multiple low energy
conformers
Rigid body docking

• Secondary conformers generated by twisting about
  rotatable bonds of the low energy monomer
  conformers
• User defined parameters
• Max deviation
• Sampling of dihedral angles
• Max penalty

Primary monomer conformers generated by (a)
CORINA ROTATE (b) sampling discrete dihedral
angles around formed bonds
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Product Generation III.
Step III.

• Docking rejection of conformers with
• High internal energy
• Boundary violation

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Multiple Extension Points Combinatorial Problem

• Clients-Master-Slaves architecture
• Mixed SGI/Linux cluster network (TCP/IP socket
  network communication)

Linux
SGI

Client1
Client2
Client3
Master

Each slave performs optimization on different
core monomer combination
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Case Study (CDK2)
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Case Study (CDK2)
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Case Study (CDK2)
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Case Study (CDK2)
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Case Study (Generated Products)
-7.95
-7.47
-7.82
-7.56
-7.75
-7.45
-7.60
-7.07
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Monomer Replacement

• Many lead compounds are composed of readily
  available starting materials (monomers) linked by
  reliable high yielding reactions
• Retrosynthetic analysis can be used to identify
  the monomers
• Structurally related analogues could be generated
  by exhaustive monomer replacement
• Considerable efficiency gains if monomer library
  is arranged in a hierarchy based on substructural
  relationships

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Hierarchy Construction
Amide
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Hierarchy Usage
Amide
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Monomer Replacement
Do they exist in starting materials HIERARCHY?
Retro-synthetic analysis
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CASE STUDY Optimisation of SPROUT designed
inhibitors of p falciparum Dihydro-orotate
Dehydrogenase using Monomer Replacement
Initial lead compound MD-155 Sprout score -7.88
Retrosynthetic analysis finds amide formation and Ullmann/Suzuki reaction for monomer formation Monomer library aryl halides and p-halo-anilines 2D structures 1923 conformations 26916
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High scoring monomer replacement resultsMonomer
replacement gave 840 new structures (including
multiple conformers of the same structure)
Scores 7.50 to 9.30.
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Experimental Results for Some Ligands Suggested
by SPROUT LeadOpt Monomer Replacement
Starting Point
MD-155 PfDHODH Ki 3.0 mM  HsDHODH Ki 11.0 nM  MD-204 PfDHODH Ki 733 nM HsDHODH Ki 21.0 nM 4 fold enhancement in Ki for PfDHODH MD-213 PfDHODH Ki 478 nM HsDHODH Ki 21.7 nM 6 fold enhancement in Ki for PfDHODH
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Conclusions

• Scoring functions for assessment of binding
  affinity of the hypothetical compounds produced
  by de novo design are far from perfect
• Hence only readily synthesisable putative ligands
  will undergo experimental evaluation by medicinal
  chemists
• Assessment of synthetic feasibility is a
  tractable problem

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Acknowledgements

• Matt Davies, Phil Bone and Timo Heikkala for
  experimental work
• Molecular Networks GmbH for providing CORINA
  ROTATE
• MDL for providing MDDR, one of the databases
  used in the complexity analysis project
• for sponsoring the lead optimization
  project