Novel Approaches To Molecular Similarity

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Novel Approaches To Molecular Similarity

• Andreas Bender, ab454_at_cam.ac.uk
• Unilever Centre for Molecular Informatics,
• University of Cambridge, UK

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Outline

• Molecular Similarity What is it and why is it
  relevant?
• Our approach to molecular similarity Finding
  many active compounds vs. generalizability?
• How to compare molecules
• The algorithm
• Results
• Special Feature Discovery of Binding Patterns

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Similarity Searching

• Complementary approach to substructural searching
  
• In substructure searching exact retrieval of a
  subgraph of a molecule is performed
• In similarity searching, an abstract molecular
  representation in descriptor space is calculated
  which is compared to abstract representations of
  other molecules
• For reviews see e.g.
• Bender, A. and Glen, R.C., Org. Biomol. Chem.,
  2004, (2) 3204 3218.
• (freely available from www.cheminformatics.org)

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Molecular Similarity The way it should be and
the way it is

• The God of Molecular Similarity (according to
  Google Picture Search)
• The Molecular Similarity Principle
• Small structural changes cause small property
  differences
• Basis of all current structure-property
  predictions
• A is active, B is not how about C?
• Solubility of A is known how does it change if
  we add group X here?

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Does the Molecular Similarity Principle work (1)?
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Does the Molecular Similarity Principle work (2) ?
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The importance of shape
Slides courtesy of Hugo Kubinyi, Erlangen
Lectures see http//www.cheminformatics.org -gt
Links -gt Education
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Is it possible and sensible to define molecular
similarity?

• YES, but one needs to be careful
• Similarity depends on the Context (e.g. the
  particular receptor easy in case of
  non-directional properties, e.g. logP)
• Similar changes may have different (even
  detrimental!) effects, depending on system
• Chiral molecules may have totally different
  activities sometimes problematic to capture

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Our approaches to molecular similarity

• How to describe and compare molecules
• Description of the system in a suitable form
• Selection of important features
• Model generation and prediction
• Results
• Special Feature Discovery of Binding Patterns

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Descriptor Choice
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2D Environment around an atom (MOLPRINT 2D,
a.k.a. Atom Environments)

• E.g. 6-aminoquinoline

Assign Sybyl mol2 atom types find
connections find connections to
connections create a tree down to n levels bin
the atom types for each level create a
fingerprint for this atom
N2
Level 0 Level 1 Level 2
Car Car
Car, Car, Car
1
2
1
1
These features are created for every (heavy) atom
in the molecule (J. Chem. Inf. Comput. Sci. 2004,
44, 170-178 2004, 44, 1710-1718)
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Feature Selection

• E.g. comparing faces first requires the
  identification of key features.
• How do we identify these?
• The same applies to molecules.

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B) Information-Gain Feature Selection

• How can we select the active compounds (red)?

Red Active Green Inactive
?
?
?
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C) Naïve Bayesian Classifier (classification by
presumptive evidence)

• The next step is to identify which molecules
  belong to which class.
• Example from e-mail classification (spam
  detection)
• Training set from nerdy chemists inbox

• Assign weighting factors to individual features,
  depending on relative frequencies in training set

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Classification

• To do this we use a Naïve Bayesian Classifier
  using the features (atom environments) we have
  identified as being important.
• Ratio gt 1 Class membership 1
• Ratio lt 1 Class membership 2
• F feature vector
• fifeature elements

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Application lead discovery

• If I have one active compound will I find
  another one?
• Database MDL Drug Data Report (MDDR)
• 957 ligands selected from MDDR
• 49 5HT3 Receptor antagonists,
• 40 Angiotensin Converting Enzyme inhib. (ACE),
• 111 HMG-Co-Reductase inhibitors (HMG),
• 134 PAF antagonists and
• 49 Thromboxane A2 antagonists (TXA2)
• 574 inactives
• Briem and Lessel, Perspect Drug Discov Des
  2000, 20, 245-264.
• Calculated Hit rate among ten nearest neighbours
  for each molecule

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Comparison Single Queries
Using Tanimoto Coefficient
Using Bayesian

• Grey bars Briem and Lessel, Perspect. Drug Disc.
  Des., 2000, 20, 245-264.
• Black Bender, A., et al., J. Chem. Inf. Comput.
  Sci., 2004, 44, 1708 1718.

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Combining Information of 5 Actives
Bender, A., et al., J. Chem. Inf. Comput. Sci.,
2004, 44, 170 178.
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TXA2, Graph-based Descriptors
Query
1
2
3
4
5
6
7
Very little diversity in heterocyclic systems
no patents, no money!
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3D Environment around a surface point solvent
accessible surface using local surface properties
Central Point (Layer 0)
Points in Layer 1

• Points in Layer 2

Etc.
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Overall Performance Comparable to 2D methods


Bender, A. et al., J. Chem. Inf. Comput. Sci.
2004 (44) 170 178.
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TXA2, 7 Hits among Top 10 by MOLPRINT 3D
Query
1
2
3
4
5
6
7
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Which features are selected for classification?

• Even if your classifier works, do the selected
  features make sense?
• Set of active vs. inactive molecules
• Information Gain calculated for each feature,
  those which are much more frequent among actives
  are suspicious and might constitute the
  pharmacophore
• Look at features from HMG and TXA2

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Selected Features - HMG

• Binding Site HMG rigid lipophilic ring

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HMG-15
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TXA2
Yellow lipophilic side chains

• Yamamoto et al., J. Med. Chem. 1993 (36) 820

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TXA2-44
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TXA2-7
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Summary

• 2D Method Finds lots of active molecules but
  they are similar to what is known already
• (Bender, A., et al., J. Chem. Inf. Comput. Sci.
  (2004) 44, 170-178 Bender, A., et al., J. Chem.
  Inf. Comput. Sci., 2004, 44, 1708 1718.)
• 3D Method Find less active compounds but
  enables discovery of new chemotypes
• (Bender, A. et al., J. Med. Chem., 2004, (47)
  6569-6583.)
• Features shown to correlate with binding patterns

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Acknowledgements

• Robert C Glen (Unilever Centre, Cambridge, UK)
• Hamse Y. Mussa (Unilever Centre, Cambridge, UK)
• Stephan Reiling (Aventis, Bridgewater, USA)
• David Patterson (Tripos)
• Software
• GRID, CACTVS, gOpenMol many, many others
• Funding
• The Gates Cambridge Trust, Unilever, Tripos