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Similarity Methods. C371 Fall 2004. Limitations of Substructure Searching/3D Pharmacophore Searching. Need to know what you are looking for Compound is either there or not Don’t get a feel for the relative ranking of the compounds Output size can be a problem. Similarity Searching.

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similarity methods

Similarity Methods


Fall 2004

limitations of substructure searching 3d pharmacophore searching
Limitations of Substructure Searching/3D Pharmacophore Searching
  • Need to know what you are looking for
  • Compound is either there or not
    • Don’t get a feel for the relative ranking of the compounds
  • Output size can be a problem
similarity searching
Similarity Searching
  • Look for compounds that are most similar to the query compound
  • Each compound in the database is ranked
  • In other application areas, the technique is known as pattern matching or signature analysis
similar property principle
Similar Property Principle
  • Structurally similar molecules usually have similar properties, e.g., biological activity
  • Known also as “neighborhood behavior”
  • Examples: morphine, codeine, heroin
  • Define: in silico
    • Using computational techniques as a substitute for or complement to experimental methods
advantages of similarity searching
Advantages of Similarity Searching
  • One known active compound becomes the search key
  • User sets the limits on output
  • Possible to re-cycle the top answers to find other possibilities
  • Subjective determination of the degree of similarity
applications of similarity searching
Applications of Similarity Searching
  • Evaluation of the uniqueness of proposed or newly synthesized compounds
  • Finding starting materials or intermediates in synthesis design
  • Handling of chemical reactions and mixtures
  • Finding the right chemicals for one’s needs, even if not sure what is needed.
subjective nature of similarity searching
Subjective Nature of Similarity Searching
  • No hard and fast rules
  • Numerical descriptors are used to compare molecules
  • A similarity coefficient is defined to quantify the degree of similarity
  • Similarity and dissimilarity rankings can be different in principle
similarity and dissimilarity
Similarity and Dissimilarity

“Consider two objects A and B, a is the number of features (characteristics) present in A and absent in B, b is the number of features absent in A and present in B, c is the number of features common to both objects, and d is the number of features absent from both objects. Thus, c and d measure the present and the absent matches, respectively, i.e., similarity; while a and b measure the corresponding mismatches, i.e., dissimilarity.” (Chemoinformatics; A Textbook (2003), p. 304)

2d similarity measures
2D Similarity Measures
  • Commonly based on “fingerprints,” binary vectors with 1 indicating the presence of the fragment and 0 the absence
  • Could relate structural keys, hashed fingerprints, or continuous data (e.g., topological indexes that take into acount size, degree of branching, and overall shape)
tanimoto coefficient
Tanimoto Coefficient
  • Tanimoto Coefficient of similarity for Molecules A and B:

SAB = c _

a + b – c

a = bits set to 1 in A, b = bits set to 1 in B, c = number of 1 bits common to both

Range is 0 to 1.

Value of 1 does not mean the molecules are identical.

similarity coefficients
Similarity Coefficients
  • Tanimoto coefficient is most widely used for binary fingerprints
  • Others:
    • Dice coefficient
    • Cosine similarity
    • Euclidean distance
    • Hamming distance
    • Soergel distance
distance between pairs of molecules
Distance Between Pairs of Molecules
  • Used to define dissimilarity of molecules
  • Regards a common absence of a feature as evidence of similarity
when is a distance coefficient a metric
When is a distance coefficient a metric?
  • Distance values must be zero or positive
    • Distance from an object to itself must be zero
  • Distance values must be symmetric
  • Distance values must obey the triangle inequality: DAB ≤ DAC + DBC
  • Distance between non-identical objects must be greater than zero.
  • Dissimilarity = distance in the n-dimensional descriptor space
size dependency of the measures
Size Dependency of the Measures
  • Small molecules often have lower similarity values using Tanimoto
  • Tanimoto normalizes the degree of size in the denominator:

SAB = c _

a + b – c

other 2d descriptor methods
Other 2D Descriptor Methods
  • Similarity can be based on continuous whole molecule properties, e.g. logP, molar refractivity, topological indexes.
  • Usual approach is to use a distance coefficient, such as Euclidean distance.
maximum common subgraph similarity
Maximum Common Subgraph Similarity
  • Another approach: generate alignment between the molecules (mapping)
  • Define MCS: largest set of atoms and bonds in common between the two structures.
  • A Non-Polynomial- (NP)-complete problem: very computer intensive; in the worst case, the algorithm will have an exponential computational complexity
  • Tricks are used to cut down on the computer usage
reduced graph similarity
Reduced Graph Similarity
  • A structure’s key features are condensed while retaining the connections between them
  • Cen ID structures with similar binding characteristics, but different underlying skeletons
  • Smaller number of nodes speeds up searching
3d similarity
3D Similarity
  • Aim is often to identify structurally different molecules
  • 3D methods require consideration of the conformational properties of molecules
3d alignment independent methods
3D: Alignment-Independent Methods
  • Descriptors: geometric atom pairs and their distances, valence and torsion angles, atom triplets
  • Consideration of conformational flexibility increases greatly the compute time
  • Relatively fewer pharmacophoric fingerprints than 2D fingerprints
    • Result: Low similarity values using Tanimoto
  • A structural abstraction of the interactions between various functional group types in a compound
  • Described by a spatial representation of these groups as centers (or vertices) of geometrical polyhedra, together with pairwise distances between centers
3d alignment methods
3D: Alignment Methods
  • Require consideration of the degrees of freedom related to the conformational flexibility of the molecules
  • Goal: determine the alignment where similarity measure is at a maximum
3d field based alignment methods
3D: Field-Based Alignment Methods
  • Consideration of the electron density of the molecules
    • Requires quantum mechanical calculation: costly
    • Property not sufficiently discriminatory
3d gnomonic projection methods
3D: Gnomonic Projection Methods
  • Molecule positioned at the center of a sphere and properties projected on the surface
  • Sphere approximated by a tessellated icosahedron or dodecahedron
  • Each triangular face is divided into a series of smaller triangles
finding the optimal alignment
Finding the Optimal Alignment
  • Need a mechanism for exploring the orientational (and conformational) degrees of freedon for determining the optimal alignment where the similarity is maximized
  • Methods: simplex algorithm, Monte Carlo methods, genetic alrogithms
evaluation of similarity methods
Evaluation of Similarity Methods
  • Generally, 2D methods are more effective that 3D
    • 2D methods may be artificially enhanced because of database characteristics (close analogs)
    • Incomplete handling of conformational flexibility in 3D databases
  • Best to use data fusion techniques, combining methods
for additional information
For additional information . . .
  • See Dr. John Barnard’s lecture at: