Here we present a new method of classifying the similar molecules using

1. Molecular similarity By: Haytham Hijazi
searching methods Advisor: Univ-Prof. Hon-Prof. Dr. Dieter
in drug discovery Roller
A Presentation in advanced graphical
engineering systems seminar 2011/2012
1

2. In this work, I propose a contribution to the field of “Cheminformatic”.
Cheminformatic means solving chemical problems using computational methods[1].
James Rhodes, Stephen Boyer1, Jeffrey Kreulen, Ying Chen, Patricia Ordonez, “Mining patents using molecular similarity
search”, IBM, Almaden Services Research, Pacific Symposium on Biocomputing 12:304-315(2007).
Molecular similarity By: Haytham Hijazi
searching methods Advisor: Univ-Prof. Hon-Prof. Dr. Dieter
in drug discovery Roller
A Presentation in advanced graphical
engineering systems seminar 2011/2012
2

3. Agenda
•The main question in this research
•The principle of similarity
•Drug discovery as an application
•Research problem
• Molecular representations (1D, 2D…)
•Searching the similarity
•Similarity coefficients calculations
•The probabilistic model (BIM)
•The contribution (MDC)
•Experiments, conclusions and discussion
3
A Presentation in advanced graphical engineering
systems seminar 2011/2012

4. “The similarity is in the eye of the beholder”
Shape Colour
Size Pattern
4

5. Question: Which molecules in a database are
similar to the query
molecule?
Application: •better compounds than initial lead
compound (Drug discovery)
•Property prediction of unknown
compound.
5

6.  Structurally similar molecules are assumed to have
similar biological properties.
 Similar biological propritiesdrug discovery.
[1]
1. Sylvaine Roy and Laurence Lafanechère, “Chemogenomics and Chemical Genetics: A User's Introduction for
Biologists, Chemists and Informaticians”, Molecular similarity, Springer Berlin, ISBN 978-3-642-19614-0, 1st Edition. 6

7. Claim: General manufacturing problems!
7

8. Similarity coefficients
Molecule
Feature selection calculations and
represntation
ranking for search
8

9.  Historical progression
◦ Complete structure
◦ Sub-Structure
 Descriptors
◦ 1D (psychophysical properties), 2D, 3D, and 4D
 Connectivity tables and graph theory!
Image Source: Karine Audouze, “Representation of molecular structures and structural
9
diversity”, ChemoInformatics in Drug Discovery, 2009.

10. SMILES
CCCC1=NN(C2=C1NC(=NC2=O)C3=C(C=
CC(=O)OC1=CC=CC=C1C(=O)O
CC(=C3)S(=O)(=O)N4CCN(CC4)C)OCC)C
SMILES – Simplified Molecular Line Entry System
Source: Karine Audouze, “Representation of molecular structures and structural
10
diversity”, ChemoInformatics in Drug Discovery, 2009.

11.  A fingerprint is a vector encoding the presence (‘1’) or
absence (‘0’) of FRAGMENT substructures in a molecule
 Dictionary based or and hash based fingerprints
Descriptor Fragment
1 AR
2 CCCCN
3 Me
9 NH2
[1]
[2]
2. Source: Karine Audouze, “Representation of molecular structures and structural diversity”,
11
ChemoInformatics in Drug Discovery, 2009.

12.  In 3D keys the position of each bit
corresponds to a certain range of distances or
angels.
 Computationally complex
Source: Karine Audouze, “Representation of molecular structures and structural
12
diversity”, ChemoInformatics in Drug Discovery, 2009.

13. Similarity coefficients
Molecule
Feature selection calculations and
represntation
ranking for search
13

14.  Exact structure search
Structure search
 Substructure search
 Similarity searching: maximal common sub
graph isomorphism, Tanimoto/Dice/Cosine
coefficients
14

15.  The similarity measure (coefficient) is a
quantitative measure of similarity
 Used to rank the results of the query
 Results are ordered decreasingly
Distance coefficients.
Probabilistic coefficients.
Correlation coefficients.
Association coefficients.
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16. Associative
Simple matching coefficient (c+d)/(a+b-c+d)
Jaccard measure (Tanimoto) c/(a+b-c) =AND/OR
Cosine, Ochiai c/√(a+b)(c+d)
Dice c/.5[(a+c)+(b+c)] and 2c/a+b
Distance
Hamming distance a+b-2c
Euclidean distance √a+b-2c
Soregel distance a+b-2c/a+b-c
Other coefficients
Pattern difference ab/(a+b c+d)2
Size (a-b)2/(a+b+c+d)2
Naomie Salim, “The study of probability model for compound similarity searching”, UTM Research
16
Management Centre Project Vote – 75207, University of Malaysia, 2009

17.  Assume we generate the fingerprint fragment
based bits
 Molecule A:
00010100010101000101010011110100
 Molecule B:
00000000100101001001000011100000
c
 Tanimoto coefficient =
 Where c=A AND B (a b) c
 Tanimoto=6/(13+8)-6=0.4
a c b
17

18.  Associate the relevance of a structure to an
explicit feature
 pi=probability that bit bi appears in an active structure.
 qi=probability that bit bi appears in an inactive structure
 αi represents a binary selector. If αi=1 means the bit occurs in the structure, else it is 0 and negated.
 P (A|S) is the probability of an active structure given S.
 P (NA|S) is the probability of an inactive structure given S.
 P(A) is the probability of ACTIVEs
 P(NA) is the probability of INACTIVES
Naomie Salim, “The study of probability model for compound similarity searching”, UTM Research
18
Management Centre Project Vote – 75207, University of Malaysia, 2009

19. Claim: General manufacturing problems !
19

20. Molecular
dynamic
simulating
tool Active
compounds
Database
Psychophysical properties Voting Class 1
Classification Class 2
Algorithm
Class n
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21.  Better insight about the similarity in terms of
bioactivity, toxicity, reactivity...(+)
 The time of searching (+)
 Prediction and voting possibilities (+)
 Cost of simulation tools (-)
 Classification errors (-)
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22.  Materials Explorer
 Itemtracker -Freezer/Cryogen sample tracking system
 CHARMM
 MDynaMix
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23. Fingerprint time gneration
30
25
20
Time (Ms) 15
2 bits
10
3 bits
5 4 bits
4 bits
0
3 bits
4 2 bits
5
6
7
8
Max path.length
Consider if we have more than 1000 bits!
Data source: simulating tool indicated in the report [17]
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24. Hit rate
0.18
0.16
0.14
0.12
0.1
Hit Rate
0.08
Hit rate
0.06
0.04
0.02
0
0 500 1000 1500 2000 2500
Selection Size
The more we increase the size of features, the more the hit rate of finding actives decreaes.
Data source: simulating tool indicated in the report [17]
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25.  Even fingerprint fragment based is time
consuming
 Probabilistic models and machine learning
introduced substantial changes
 Mixing more than type of descriptors seems
efficient i.e. Time and results quality
 Still need to have experimental results
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26. Molecular similarity Thanks for your listening
searching methods
in drug discovery Haytham Hijazi

A Presentation to the advanced graphical
engineering systems seminar 2011/2012
26