IN SILICO TARGET PREDICTION BY TRAINING NAÏVE BAYESIAN
MODELS ON CHEMOGENOMICS DATABASES
Nidhi
Submitted to the faculty of the Chemical Informatics Graduate Program
in partial fulfillment of the requirements
for the degree
Master of Science
in the School of Informatics,
Indiana University
December 2005
Accepted by the Faculty of Indiana University, in partial
fulfillment of the requirements for the degree of Master of Science
________________________
Mahesh Merchant, Ph.D., Chair
_______________________
Jeremy Jenkins, Ph.D.
_______________________
Douglas Perry, Ph.D.
ii
ACKNOWLEDGEMENTS
I extend my gratitude and appreciation to the people who made this master’s
thesis possible. My foremost thanks go to my research advisor, Dr. Jeremy Jenkins. I
want to thank him for his insights and suggestions that helped me have a deeper
understanding of the subject matter.
I am grateful to Dr. Mahesh Merchant for the support and encouragement that I
received from him throughout this project. I am indebted to Dr. Douglas Perry for his
invaluable guidance and support right from the very beginning of my work. I would also
like to acknowledge my manager Dr. John Davies and colleague Dr. Meir Glick at
Novartis Institutes for Biomedical Research for their constant feedback on my work
which made me strive to do better.
Many thanks go to the faculty and staff at School of Informatics. These include
Dr. Kelsey Forsythe, Dr. David Wild, Ms. Mary O’Neil, Ms.Vickie Bucker and Mr. Dale
Ray.
Special thanks are due to MDL for awarding me with the Elsevier MDL
Excellence in Informatics fellowship. Last but not the least, I thank my parents and my
brother for always being there for me and supporting me in all my endeavors.
iii
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ............................................................................................... iii
LIST OF TABLES .............................................................................................................. v
LIST OF FIGURES ........................................................................................................... vi
INTRODUCTION .............................................................................................................. 1
METHODS ......................................................................................................................... 9
RESULTS ......................................................................................................................... 18
DISCUSSION ................................................................................................................... 32
CONCLUSION ................................................................................................................. 35
REFERENCES ................................................................................................................. 36
APPENDIX ....................................................................................................................... 39
iv
LIST OF TABLES
Table 1. Datasets used for training and testing the naïve Bayesian models
Table 2. MDDR activity classes used for study
Table 3. Murcko assemblies for COX-2 inhibitors derived from WOMBAT database
Table 4. Biggest target predicted by naïve Bayes model for MDDR generic activity
classes
v
LIST OF FIGURES
Figure 1a. Chemogenomics approach to drug discovery
Figure 1b. Organization of a Chemogenomics Database
Figure 2a. Substructure search vs Extended connectivity fingerprint structure search
Figure 2b. Extended Connectivity Fingerprint generation method
Figure 3. Pipeline Pilot protocol for building multiclass naïve Bayes model
Figure 4. Murcko assembly distribution of MDDR and WOMBAT.
Figure 5a-b. Percentage of test compounds with order of prediction for WOMBAT85%
model
Figure 6a. Percentage of test compounds derived from ten MDDR activity classes with
order of prediction for WOMBAT model
Figure 7a. Top target distribution for MDDR activity class Antineoplastic
Figure 7b. Top target distribution for MDDR Kinases
Figure 7c. Top target distribution for MDDR activity class Antiinflammatroy, Intestinal
Figure 7d. Top target distribution for MDDR activity class Antihypertensive
Figure 7e. Top target distribution for MDDR activity class Antiarithritic
Figure 8a. MDDR Extreg: 142057 (N-(2-aminophenyl)-4-(3-hydroxypropanamido)
benzamide). A substructure in many compounds with anti-cancer activity and predicted
as a HDAC inhibitor.
Figure 8b. MDDR Extreg 142055 (N-(2-aminophenyl)-4-isobutyramidobenzmide).
Analog of 142057 and predicted as a HDAC inhibitor
Figure 8c. N-aryl benzamide class that is patented as a HDAC inhibitor class
Figure 9a. Trichostatin D. MDDR Extreg: 286017. Predicted as a HDAC inhibitor
vi
Figure 9b. Trichostatin A. Known HDAC inhibitor
Figure 10. Closest similarity histogram for MDDR and WOMBAT COX-1 inhibitors
Figure 11. Tanimoto similarity coefficient vs. the order of prediction for MDDR Kinases
vii
INTRODUCTION
The completion of Human Genome Project is seen as a gateway to the discovery of novel
drug targets (Jacoby, Schuffenhauer, & Floersheim, 2003).
How much of this
information is actually translated into knowledge, e.g., the discovery of novel drug
targets, is yet to be seen. The traditional route of drug discovery has been from target to
compound. Conventional research techniques are focused around studying animal and
cellular models which is followed by the development of a chemical concept. Modern
approaches that have evolved as a result of progress in molecular biology and genomics
start out with molecular targets which usually originate from the discovery of a new gene
.Subsequent target validation to establish suitability as a drug target is followed by high
throughput screening assays in order to identify new active chemical entities (Hofbauer,
1997).
In contrast, chemogenomics takes the opposite approach to drug discovery
(Jacoby, Schuffenhauer, & Floersheim, 2003). It puts to the forefront chemical entities as
probes to study their effects on biological targets and then links these effects to the
genetic pathways of these targets (Figure 1a). The goal of chemogenomics is to rapidly
identify new drug molecules and drug targets by establishing chemical and biological
connections. Just as classical genetic experiments are classified into forward and reverse,
experimental chemogenomics methods can be distinguished as forward and reverse
depending on the direction of investigative process i.e. from phenotype to target or from
target to phenotype respectively (Jacoby, Schuffenhauer, & Floersheim, 2003). The
identification and characterization of protein targets are critical bottlenecks in forward
1
Homologous
Proteins
Gene
Families
Genomics/ Proteomics
Drug
Chemotypes
Chemoproteomics
Figure 1a. Homologous proteins are characterized by studying gene families and traditional drug discovery
route is from protein targets to drug molecules. Chemogenomics explores the relationship between
structure (“chemical genotype”) and biological activity (“chemical phenotype”) and establish genetic
connection based on chemical phenotype.
Protein1
Compound1
Compound3
S
i
m
i
l
a
r
i
t
y
Cheminformatics
Bioinformatics
Protein2
Compound2
Figure 1b.Cheminformatics techniques are based on structural similarity between molecules while
bioinformatics deals with sequence similarity to establish homologous proteins. Chemogenomics can be
used to derive the missing links between compounds and targets as well as genetic connections between
two targets.
2
chemogenomics experiments. Currently, methods such as affinity matrix purification
(Taunton, Hassig, & Schreiber, 1996) and phage display (Sche, McKenzie, White, &
Austin, 1999) are used to determine targets for compounds.
None of the current
techniques used for target identification after the initial screening are efficient.
In silico methods can provide complementary and efficient ways to predict targets
by using chemogenomics databases to obtain information about chemical structures and
target activities of compounds. Annotated chemogenomics databases integrate chemical
and biological domains and can provide a powerful tool to predict and validate new
targets for compounds with unknown effects (Figure 1b). A chemogenomics database
contains both chemical properties and biological activities associated with a compound.
The MDL Drug Data Report (MDDR) (Molecular Design Ltd., San Leandro, California)
is one of the well known and widely used databases that contains chemical structures and
corresponding biological activities of drug like compounds. The relevance and quality of
information that can be derived from these databases depends on their annotation
schemes as well as the methods that are used for mining this data. In recent years
chemists and biologist have used such databases to carry out similarity searches and
lookup biological activities for compounds that are similar to the probe molecules for a
given assay. With the emergence of new chemogenomics databases that follow a wellstructured and consistent annotation scheme, new automated target prediction methods
are possible that can give insights to the biological world based on structural similarity
between compounds. The usefulness of such databases lies not only in predicting targets,
but also in establishing the genetic connections of the targets discovered, as a
consequence of the prediction.
3
The ability to perform automated target prediction relies heavily on a synergy of
very recent technologies, which includes:
i) Highly structured and consistently annotated chemogenomics databases. Many such
databases have surfaced very recently; WOMBAT (Sunset Molecular Discovery LLC,
Santa Fe, New Mexico), KinaseChemBioBase (Jubilant Biosys Ltd., Bangalore, India)
and StARLITe (Inpharmatica Ltd., London, UK), to name a few.
ii) Chemical descriptors (Xue & Bajorath, 2000) that capture the structure-activity
relationship of the molecules as well as computational techniques (Kitchen, Stahura, &
Bajorath, 2004) that are specifically tailored to extract information from these
descriptors.
iii) Data pipelining environments that are fast, integrate multiple computational steps, and
support large datasets.
A combination of all these technologies may be employed to bridge the gap
between chemical and biological domains which remains a challenge in the
pharmaceutical industry.
4
BACKGROUND
The current trend in drug discovery is to focus on gene families, which makes it possible
to work with multiple targets simultaneously, because of their genetic interrelationship.
Chemogenomics seeks to apply this relationship by starting with chemical compounds to
find novel targets. In silico methods can be used to further advance this field. A number
of different in silico approaches are possible, depending on the objective of a particular
study. The most widely used method to make a selection is to carry out similarity
searches on a structural compound database with reference molecules (Hert et al., 2004).
Cutoff values derived from similarity metrics like the Tanimoto coefficient (Willett,
2005) can be used to filter uninteresting compounds. Three-dimensional pharmacophorebased searches can also be used when 3-D structural information and active ligands are
available (Toledo, Lydon, & Elbaum, 1999).
Classification methods are useful to generate focused libraries as well as identify
novel compounds from large compound collections. Genetic algorithms (GA) (Gillet,
Willett, & Bradshaw, 1998), neural networks (NN) (Manallack et al., 2002), support
vector machines (SVM) (Weinstein et al., 1997), recursive partitioning (RP) (P. Blower,
Fligner, Verducci, & Bjoraker, 2002; Rusinko, Farmen, Lambert, Brown, & Young,
1999), naïve Bayes classifier (NB) (Klon, Glick, & Davies, 2004; Xia, Maliski, Gallant,
& Rogers, 2004), and standard and partial least square methods (Zhu, Hou, Chen, & Xu,
2001) are some of the classification techniques that have gained popularity among
researchers to model structure-activity relationships of compounds.
Clustering methods are also widely used to group compounds with similar
bioactivity based on their structural information (Shemetulskis, Dunbar, Dunbar,
5
Moreland, & Humblet, 1995). A number of hierarchical methods such as Wards and
group-average and non hierarchical clustering methods such as Jarvis Patrick were
compared in a study conducted by Brown and Martin (Brown & Martin, 1998). The
Wards clustering method is reported to perform best in separating active from inactive
compounds in this study. K-means is another nonhierarchical clustering method that has
been reported to perform well in a study to identify ligand binding sites on proteins
(Glick, Robinson, Grant, & Richards, 2002).
The performance of a particular similarity search, clustering or classification
method in terms of predicting activity for a given compound depends on the dataset as
well the choice of descriptors. Numerous descriptors have been developed to describe
molecules: 1-D descriptors such as molecular weight, Simplified Molecular Input Line
Entry Specification (SMILES), and melting point, 2-D descriptors such as connectivity
tables and 2-D fingerprints, and 3-D descriptors such as pharmacophore definition triplets
(PDTs) (Abrahamian et al., 2003) have been used to represent chemical structures. 2-D
fingerprinting is by far the most commonly used structure abstraction technique. A recent
study done by Hert et al. to compare a number of 2-D fingerprints such as BCI, Daylight,
Extended Connectivity fingerprints (ECFPs), Unity and Avalon shows the effectiveness
of ECFPs, which are based on circular substructures, over other topological descriptors
(Hert et al., 2004). Also, Klon et al. have reported considerable enrichment in high
throughput docking data by using ECFPs in combination with Naïve Bayesian classifier
(Klon, Glick, & Davies, 2004).
Modeling biological activities based on chemical structure abstraction for a wide
range of compounds has been attempted recently in the form of a computer program,
6
PASS (Lagunin, Stepanchikova, Filimonov, & Poroikov, 2000) that has also been
integrated with the National Cancer Institute’s open database browser (Poroikov et al.,
2003). PASS makes use of substructure descriptors called multilevel neighborhoods of
atoms (MNA) (Fomenko, Sobolev, Filimonov, & Poroikov, 2003).
After an initial
training with known biologically active compounds, it can be used to predict a wide
range of biological activities such as mutagenicity, carcinogenicity, and embryotoxicity
for test compounds.
The usefulness of annotated chemical libraries, overlaying the ligand-target
domains, has been recently recognized. Studies analyzing ligand-target space (Weinstein
et al., 1997) and finding correlation between substructures and gene expression patterns
(P. E. Blower et al., 2002) have been reported. Kohonen self-organizing maps were
recently employed in an attempt to classify 20,000 compounds tested against 80 of NCI’s
tumor cell lines (Rabow, Shoemaker, Sausville, & Covell, 2002) with very useful results.
In another effort to integrate bio- and cheminformatics domains, annotation schemes for
ligands of four target classes (enzymes, G protein-coupled receptors, nuclear receptors
and ligand-gated ion channels) have been proposed (Schuffenhauer et al., 2002).
The amalgamation of a robust fingerprinting method and an appropriate
computational model trained on a well-annotated chemogenomics database can be an
extremely useful tool to discover novel targets for compounds with unknown activities.
Further, the structure-activity relationship homology principle (Frye, 1999) can be
applied to discover leads for homologous targets.
7
Hypothesis
It is possible to predict targets for compounds with unknown activities by training
multiple naïve Bayes classifiers on compounds with known biological targets. The intent
of this research is to train multiple Laplacian-modified naïve Bayes models with extended
connectivity fingerprints on compound records derived from the WOMBAT database in
order to predict targets for test compounds derived from the MDDR database.
8
METHODS
Extended Connectivity Fingerprints
Extended connectivity fingerprints, developed by SciTegic (SciTegic, San Diego,
California), are structure fingerprints based on the Morgan algorithm (Morgan, 1965).
An ECFP feature represents an exact structure with limited and specified attachment
points. Therefore, a substructure search using the fragment para-substituted benzene (a)
would return compounds (b, c) while a search with ECFPs would return only (c) (Figure
2a). This is because for ECFPs, (b) does not conatin (a) as there is substitution on the
benzene ring at locations other than the specified attachment atoms marked as X.
a
b
c
Figure 2a Substructure search vs Extended connectivity fingerprint structure search
ECFPs are generated in an iterative fashion. Initially, each atom is assigned a
code that is derived from the number of connections to the atom, element type, charge
and mass of the atom which corresponds to ECFP with a neighborhood size of “0”. In
the first iteration, information about each atom’s immediate neighbors is collected and a
new code representing the atom and its immediate neighbors is generated. In each
iteration, the neighborhood size becomes larger and the updated codes of the atoms from
previous iterations are used for assigning new codes. When the desired neighborhood
size is reached, the set of all features is returned as a fingerprint (Figure 2b).
9
Figure 2b Extended connectivity fingerprint generation method
ECFPs with a neighborhood size of 6 (i.e., ECFP_6) were used as structural descriptors
to train the naïve Bayes classifiers as they represent a fairly large set of features or
structural units that are crucial for structural comparison of compounds (Klon, Glick,
Thoma, Acklin, & Davies, 2004).
Multiclass Naïve Bayesian Modeling
Naïve Bayes
Naïve Bayes, a statistical classification method, can be efficiently used for large datasets
as it scales linearly with the number of molecules.
It is based on Bayes rule of
conditional probability (Weisstein) which states that given two events A and B, the
probability of occurrence of event A given that event B has already occurred, P(A|B), is
given by:
P(A|B) =P(B|A)P(A)/P(B)
where P(A) and P(B) are the probability of occurrence of events A and event B,
respectively.
In this study, naïve Bayes is used to classify compounds into actives and inactives
against a particular target based on the frequency of occurrence of a given feature, which
is encoded by extended connectivity fingerprint bits. Given an ECFP, the probability of a
10
given compound being active against a particular target that contains ECFP, is given by
P(active|ECFP), where event active refers to the event that the compound is active. Thus,
P(active|ECFP) = P(ECFP|active) P(active)/P(ECFP)
This method is said to be naïve because it naively assumes independence of the features
from one another. Making this assumption, it is valid to multiply the individual feature
probabilities. Given n features or ECFP bits,
P(active|ECFP) = P(ECFP1|active) X
P(ECFP2|active) X
P(ECFPn|active)P(active)/P(ECFP)
Laplacian Correction
A Laplacian correction is used as different features are sampled at different frequencies.
If N is the number of compounds in a given dataset, of which M are active, the baseline
probability of a compound being active is given by:
P(active) = M/N
The probability of a compound being active given feature F present in B number of
compounds of which A are active can be given by
P(active|F) = A/B
As the number of compounds B diminishes, the probability estimate of a feature starts
getting biased. For example if A =1 and B =1 then P(active|F) = 1, which is not true as
the feature is undersampled.
11
To correct for the above anomaly, it is assumed that most of the features have no
relationship with activity, i.e., for most Fi, P(active|Fi) = P(active), which is the baseline
probability. If the feature is sampled X additional times, then P(active) X X of those new
samples should be active. Therefore, each feature can be corrected by adding those
virtual samples.
P(active|F) = (A + P(active) X X) / (B + X)
Now, as the number of samples, B, decreases, the feature’s probability
contribution converges to P(active).
Relative Estimator
Naïve Bayes as implemented in Pipeline Pilot is a relative estimator which is obtained by
dividing the Laplacian-corrected Bayes classifier by P(active). Thus,
P(active|F) = P(active|F)/P(active)
which implies:
i)
for most features, log P(active|F) ~ 0
ii)
for features more common in actives, log P(active|F) > 0
iii)
for features less common in actives, log P(active|F) < 0.
The Learn Molecular Categories component in Scitegic’s Pipeline Pilot software
can build multiple naïve Bayes models in one protocol. The user needs to specify a
“CategoryProperty” property on each data record, in this case target name, which is
subsequently used to create different equations for different values of that property. Once
a multiple naïve Bayes model is created, it can be added to another protocol to calculate
naïve Bayes scores for multiple targets. A test compound is passed through each of the
equations of this model and scores corresponding to each target name is calculated. The
12
highest score for a target is taken as most likely target for that compound. Similarly, the
next highest score for a target can be assigned as second most likely target and so on.
Databases and Datasets
WOMBAT
WOMBAT (World of Molecular Bioactivity) is a consistently annotated chemogenomics
database.
The version used for this study, WOMBAT 2005.1, contains 117,007
compounds and 104,230 unique SMILES. 230,000 biological activities and 1,021 unique
targets are reported for these compounds. It contains 4,786 different chemical series
from 4,773 papers published in medicinal chemistry journals between 1975 and 2004.
The database follows a hierarchical scheme such that it is possible to look up the target
family. The target families are based on the functional properties of targets. Targets are
usually proteins but can also be DNA or RNA.
For
the
purpose
of
this
study,
compounds
with
activity threshold,
IC50/EC50/Ki/Kb/Kd/MIC/ED50 < 30µm were selected. Thus, a reduced dataset of 103,735
compounds with 964 biological targets was generated. Also, WOMBAT is organized in
a way in which one compound may have more than one target. The database was
preprocessed to associate one target per compound so that there is unique value for the
“CategoryProperty” for the Learn molecular component of Pipeline Pilot. As a result,
192,373 compound records were generated because of duplication. This dataset will be
referred to henceforth as the WOMBAT database.
MDDR
MDDR contains information about biologically relevant compounds. Many biological
activities reported in MDDR are very generic e.g. antineoplastic, antihypertensive and
13
anti-inflammatory. It covers information available in patent literature, journal articles,
and meetings. MDDR is updated annually. The version used for this study contains
159,967 compounds and 761 biological activity classes.
There are 2,789 compounds in MDDR with no structural information. Since the
descriptors used for statistical analysis in this study are based on structural information,
these compounds were removed from the dataset and the remaining 156,873 compounds
with 659 biological activity classes were kept for the study.
Datasets
We built two separate multiclass Bayes models for the purpose of this study. The
WOMBAT 85% Model has 85% of WOMBAT compound records as training set and the
other 15% is used as the test set. Cross validation of WOMBAT database was carried out
because there are large number of records available for the study. There is no duplication
of compounds across two sets.
The second model, the WOMBAT model, uses
WOMBAT as the training set. Test sets are derived from ten MDDR specific biological
activity classes and ten generic biological activity classes. Table 1 summarizes the
number of compounds and respective databases from which training and test sets are
extracted for both models. Table 2 shows the MDDR activity classes with the total
number of compound records corresponding to each class.
Model Name
Wombat85% Model
Wombat Model
Number of compounds
Training Set
Test Set
162238
30135
(WOMBAT)
(WOMBAT)
192373
53137
(WOMBAT)
(MDDR)
Table 1. Datasets used for training and testing the naïve Bayesian models
14
MDDR Activity Class
Number of Compounds
Phosphodiesterase IV Inhibitor
2000
Cyclooxygenase-1 Inhibitor
88
Cyclooxygenase-2 Inhibitor
1055
Acetylcholinesterase Inhibitor
810
Angiotensin II AT1 Antagonist
2185
Angiotensin II AT2 Antagonist
53
ACE Inhibitor
570
Reverse Transcriptase Inhibitor
819
HIV-1 Protease Inhibitor
1027
H+/K+-ATPase Inhibitor
751
Antineoplastic
19621
Kinases*
1858
Anti-inflammatory, Intestinal
29
Antihypertensive
11124
Anti-arthritic
11147
Table 2. MDDR activity classes used for study
*Kinases are derived from six MDDR activity classes viz. Adenosine Kinase Inhibitor, Nucleoside
Diphosphate Kinase Inhibitor, Phosphatidylinositol Kinase Inhibitor, Protein Kinase C Inhibitor,
Thymidine Kinase Inhibitor and Tyrosine-Specific Protein Kinase Inhibitor
Software
Scitegic’s Pipeline Pilot™ version 4.5.2 was employed to build the multiclass naïve
Bayesian models.
Hardware
The computations were carried out on a Pipeline Pilot server with Intel® Xeon™ 2.7
GHz dual processors and 4 GB of RAM.
15
Protocol for Building Multiclass Naïve Bayesian Models
Figure 3 shows the components used by the Pipeline Pilot protocol that was used to build
multiclass Bayesian models. The preprocessing steps involved:
a. Reading of the compound library into the protocol
b. Conversion to Pipeline Pilot molecule format from SMILES string
c. Removal of salts if any from the molecule
d. Standardization of stereo-atoms and charges
e. Reformatting of text string contained in target name to remove any occurrences of
single quotes
f. Calculation of extended connectivity fingerprints for all molecules
Figure 3 Pipeline Pilot protocol for building multiclass naïve Bayes models
The protocol generates a new component called Learn Molecular properties. This
component can be used in another protocol with test data as input to this component.
Diversity Analysis
Diversity analysis is carried out to ensure that the model is applicable over a wide range
of structural classes. An analysis of structural diversity among the training and test set
can thus establish the effectiveness of the computational model. The chemical diversity
was assessed by carrying out Murcko assemblies analysis (Bemis & Murcko, 1996) of
WOMBAT and MDDR compounds. Murcko assemblies are obtained by removing the
side chains from the molecule and keeping the core ring system and associated atom type,
16
bond order and hybridization information. Table 3 shows examples of COX-2 inhibitors
derived from the WOMBAT database, and their corresponding Murcko assemblies.
After generating Murcko assemblies for all the compounds in WOMBAT and MDDR,
unique as well as assemblies common to both can be determined. Based on the number
of unique assemblies it can be determined if two datasets differ significantly in terms of
chemical diversity.
Compound Structure
Murcko Assembly
Table 3. Murcko assemblies for COX-2 inhibitors derived from WOMBAT database.
17
RESULTS
Diversity Analysis
Figure 4 shows the results of the diversity analysis based on Murcko scaffold analysis. It
shows that there are 65,806 Murcko assemblies unique to MDDR, 24,772 assemblies
unique to WOMBAT and 6,395 assemblies common to both datasets. Also, the figure
depicts the frequency of assemblies in the datasets, e.g., the bar region corresponding to
blue accounts for Murcko assemblies that occur just once in the complete dataset
(singletons). The red region corresponds to assemblies that occur twice in the dataset,
and so on.
The results show that MDDR is more diverse than WOMBAT, as it contains
larger number of distinct Murcko fragments, but it cannot be used for effective target
prediction simply because it is not highly annotated. There is a significant overlap
between both datasets, but in spite of that there are large numbers of Murcko fragments
unique to each database.
18
M- MDDR Scaffolds; MC, WC-Common Scaffolds in MDDR & WOMBAT; W-WOMBAT Scaffolds
Frequency of scaffold occurrence denoted by color; blue: 1 occurrence, red: 2 occurrences, yellow: 3
occurrences, black: 4 occurrences, green: 5 or more occurrences
Figure 4. Murcko assembly distribution of MDDR and WOMBAT
Method Validation
Wombat 85% Model
The top 3 scores or top 3 targets were evaluated and the results were interpreted in terms
of percentage of compounds and rank in which the model predicted the right target.
Figure 5a shows the results of target prediction for the WOMBAT 85% model. This
model predicts the right target on the first guess for 82% of the compounds, on the
second guess for 8% of the compounds, and on the third guess for 2% of the compounds.
19
Also, 8% of the compounds were not correctly predicted in the top 3 guesses. A further
analysis was done to examine predicted target families. The model predicts the right
target family for 89% of the compounds in first guess as shown in, Figure 5b.
First Target Prediction right
Second Target prediction right
Third Target Prediction right
Not predicted right in top three predictions
Figure 5a Target Prediction
20
First Target Prediction right
Second Target prediction right
Third Target Prediction right
Not predicted right in top three predictions
Figure 5b Target Family Prediction
Figure 5a-b. Percentage of test compounds with order of prediction for WOMBAT85% model
WOMBAT Model
Figure 6a shows the percentage of compounds predicted with the correct target in the top
three predictions, as well as compounds for which the model failed to predict the correct
target.
Figure 6b correspondingly depicts the percentage of compounds that were
predicted in the correct target family, as well as the unsuccessful predictions for the target
family. On average, 77% of the compounds were predicted with correct targets and 79%
of the compounds were predicted with the correct family in top three predictions.
21
First Target Prediction right
Second Target prediction right
Third Target Prediction right
Not predicted right in top three predictions
Figure 6a Target Prediction
22
First Target Family Prediction right
Second Target Family prediction right
Third Target Family Prediction right
Not predicted right in top three predictions
Figure 6b Target Family Prediction
Figure 6a-b Percentage of test compounds derived from ten MDDR activity classes with order of prediction
for WOMBAT model
Qualitative Analysis
Top targets predicted for five generic activity classes, viz. Antineoplastic, Kinase,
Antiarthritic, Antihypertensive and Anti-inflammatory, Intestinal, derived from MDDR
were examined. The results were binned on target frequencies and interpreted in terms of
the type of targets assigned to the generic activities by naïve Bayes Models. Figures 7a-e
show the distributions of targets for five generic activity classes. Verification with the
23
scientific literature available on corresponding targets revealed that the predicted target
with maximum frequency of compounds was associated with the corresponding generic
activity.
Antineoplastics have tubulin, a well known antineoplastic target, as the biggest
target (8%) (Figure 7a) (Hadfield, Ducki, Hirst, & McGown, 2003). Tubulin is essential
to many vital processes such as cell division and movement of materials within a cell.
Drugs such as Taxol® bind to tubulin and cause the protein to lose its flexibility, which
prevents cell division.
Figure 7a. Target Distribution: Top targets predicted for generic MDDR activity class Antineoplastic.
Unregulated kinase activity is a frequent cause of cancer where kinases regulate
aspects that control cell growth, movement and death (Melnikova & Golden, 2004).
24
Therefore many kinases are anticancer targets, which is evident by the target distribution
for Antineoplastics.
Kinases have epidermal growth factor receptor (EGFR), which is a tyrosine
protein kinase (Mendelsohn & Baselga, 2003), as the biggest target (27%) (Figure 7b).
These receptors exist on the cell surface and are involved in initiating a signal
transduction cascade which ultimately leads to DNA synthesis and cell proliferation.
Figure 7b Target Distribution: Top targets predicted for MDDR Kinases*
The Anti-inflammatory, Intestinal activity class has NK1 as the biggest target (Figure 7c).
NK1 is a tachykinin receptor.
Tachykinin receptor antagonists have been strongly
implicated for the treatment of at least 6 major disease groups which includes CNS
disorders, pain and emesis, airway disease, urinary incontinence and intestinal
dysfunction (Khawaja & Rogers, 1996).
25
Figure 7c Target Distribution: Top targets predicted for MDDR activity class Anti-inflammatory, Intestinal
Antihypertensives have Angiotensin II type 1 (AT1) receptor as their biggest target
(Figure 7d). AT1 receptor has a well established role in maintaining blood pressure, and
water and electrolyte homeostasis (Wexler et al., 1992).
AT1 antagonists work as
antihypertensive agents by causing either arterial or mixed arterial and venous dilation.
26
Figure 7d Target Distribution: Top targets predicted for MDDR activity class Antihypertensive
The Antiarthritic class of compounds has COX-2 as the biggest target (14%). COX-2 is
an enzyme responsible for mediating pain and inflammation (Figure 7e).
inhibitors are often used to treat arthritis (Hochberg, 2005).
27
COX-2
Figure 7e Target Distribution: Top targets predicted for MDDR activity class Antiarthritic
Table 4 summarizes the biggest assigned target for each activity class along with binning
frequency and respective the target families.
MDDR Activity Class Binning Frequency Biggest Target Target Family
Antineoplastic
50
Tubulin
Tubulin
Kinases*
10
EGFR
Tyrosine protein Kinase
Anti-inflammatory,
Intestinal
Antihypertensive
Not Binned
NK1
GPCR
50
AT1
GPCR
Antiarthritic
50
COX-2
Prostaglandin G/H
synthase, oxidoreductase,
peroxidase, dioxygenase
Table 4. Biggest target as predicted by naïve Bayes Models for MDDR generic activity classes
28
Examples of compounds with right prediction
Histone Deacytlase (HDAC) inhibitors have been recently recognized as anticancer
agents due to their role in the regulation of transcription (Vigushin & Coombes, 2002).
HDAC, when inhibited, stops differentiation and induces cell cycle arrest.
For nearly 2% of MDDR compounds with antineoplastic activity, HDACs are
predicted as top targets by naïve Bayesian Models (Figure 7a) though MDDR contains no
information on specific targets for these compounds. I here report three such compounds
which have high structural similarity to the known HDAC inhibitors.
i) N-(2-aminophenyl)-4-(3-hydroxypropanamido) benzamide (Figure 8a)
MDDR Extreg 142057 is predicted as a HDAC inhibitor. This compound is a
substructure in several other compounds that have antineoplastic activity. Also,
one of these compounds belongs to a class of compounds known as N-aryl
benzamides which are synthetic HDAC inhibitors (D. F. Schuppan, 2003).
Figure 8c shows the Markush representation of the entire class and example of a
compound belonging to that class.
ii) N-(2-aminophenyl)-4-isobutyramidobenzmide (Figure 8b) MDDR
Extreg 142055 is an analogue of N-(2-aminophenyl)-4-(3-hydroxypropanamido)
benzamide and occurs as a substructure in various compounds with anticancer
activity. HDAC is predicted as the top target for this compound.
29
Figure 8a N-(2-aminophenyl)-4-(3-hydroxypropanamido) benzamide
Figure 8b N-(2-aminophenyl)-4-isobutyramidobenzmide
Figure 8c N-aryl benzamide class
iii) MDDR Extreg 286017: Trichostatin D (Figure 9a) is listed as an
Antineoplastic in MDDR with no reported literature on a specific target. The
top target for this compound as predicted by naïve Bayes Models was HDAC.
Trichostatin A (Figure 9b) is a known natural HDAC inhibitor (Vigushin et al.,
30
2001). It can be seen that both compounds have the same chemotypes except
for the extra sugar ring in Trichostatin D. A further discussion with a chemist
working on HDAC inhibitors in Novartis revealed that the compound can be
very likely a prodrug. A prodrug is a pharmacological compound which is
administered in an inactive form and is metabolized into an active form in the
body. The sugar ring would be cleaved off in vivo resulting in a hydroxamic
acid group as present in Trichostatin A.
HO
OH
H
N
N
O
O
OH
O
OH
O
Figure 9a. Trichostatin D
7-(4-dimethylaminophenyl)-4,6-dimethyl-7-oxo-N-3,4,5-trihydroxy-6- (hydroxymethyl)
tetrahydro-2H-pyran-2-yloxy) hepta-2,4-dienamide
Figure 9b. Trichostatin A
7-(4-dimethylaminophenyl)-4,6-dimethyl-7-oxo-hepta-2,4-dienehydroxamic acid
31
DISCUSSION
Naïve Bayesian modeling in conjunction with extended connected fingerprints has been
shown to perform well in identifying actives in comparison to other substructure based
searching methods (Klon, Glick, & Davies, 2004). I here investigated the application of
the Multicategory Naïve Bayes Model in predicting targets for compounds with unknown
or little known activity information. The method is applicable over a wide range of
activity classes and 77% of the test compound sets used for validation are predicted with
correct targets. The method also performs well while correlating targets with the generic
activities.
The training of the Multicategory Naïve Bayes Model with a dataset of ~190,000
compound records with 964 targets takes nearly six hours.
The subsequent target
prediction rate is about 100 minutes for 100,000 compounds. Thus, it can be efficiently
used as a complementary tool for target prediction for a big library of compounds.
Data mining algorithms are largely dependent on the training datasets. First, the
structural diversity among underlying compounds is essential to the success of such
algorithms.
In this study, COX-1 inhibitors have the lowest correct prediction
percentage.
Only ~27% of the COX-1 inhibitors are successfully predicted by the
algorithm. To account for such a low prediction, I carried out a closest Tanimoto
similarity analysis between the test set (COX-1 compound sets derived from MDDR) and
training set (COX-1 compound sets derived from WOMBAT). Figure 10 shows the
percentage of MDDR compounds with a given Tanimoto Similarity coefficient value
with WOMBAT compounds. Nearly 95% of the MDDR compounds have a Tanimoto
value <0.4 with WOMBAT COX-1 compounds.
32
Closest Similarities COX-1
% of MDDR Samples
30%
25%
20%
15%
Frequency
10%
5%
0%
0
0.2
0.4
0.6
0.8
1
Tanimoto Similarities
Figure 10. Closest Tanimoto similarity distribution for MDDR COX-1 compounds
This implies that WOMBAT COX-1 compounds do not have sufficient structural
diversity. The use of a structurally diverse dataset for training would thus result in better
predictions.
Second, high similarity between compounds in training and test sets can lead to
good predictions. Further analysis of the kinases derived from MDDR was conducted to
check how well the Bayes classifier works when there is low similarity between test and
training sets. Figure 11 is a plot of Tanimoto similarity vs. the order of prediction for
MDDR kinases. For a high similarity between the MDDR and WOMBAT, the first
prediction is almost always correct. There is a decrease in correct prediction percentage
with decrease in similarity but even for a low similarity score of 0.2-0.5, correct
predictions are made by naïve Bayes classifier. This shows that Bayes can classify data
even at low similarity values.
33
Not Predicted as Kinase in top 10 predictions
Figure 11. Tanimoto similarity coefficient and the order of correct prediction for MDDR kinases
Third, the consistency and quality of annotation in chemogenomics database is of
prime importance when it comes to deriving meaningful and reliable conclusions from
the predictions. The information contained in such databases is derived from research
data published in medicinal chemistry journals, patents, and other such sources. There is
a constant need for updating such databases as information about new targets and
compounds becomes available.
34
CONCLUSION
Naive Bayes models trained on a well-annotated chemogenomics database can be used to
predict targets for compounds with unknown activities and ascertain genetic connections
between targets. Structural diversity among compounds used for building models plays
an important role in automated target prediction methods. Therefore, using a structurally
diverse chemogenomics database as training set would result in better predictions. The
use of in silico techniques in abstracting the structure activity relationship is valid as long
as underlying information is correct and up-to-date. As new discoveries are made in the
biological and chemical world, new information should be incorporated as it becomes
available.
35
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38
APPENDIX
CURRICULUM VITAE
NIDHI
B. Sc. (chemistry), M.S. (computer applications)
Phone: 317-652-1631 Email: khurananidhi@gmail.com
SUMMARY





Solid grasp on basic sciences through an undergraduate degree in chemistry
Extensive software development skills through a master’s degree in computer applications
High level of awareness regarding software industry’s practices gained by internships and
full time job as software engineer
Pursuing a master’s degree in Chemical Informatics at Indiana University, Indianapolis.
Worked at Novartis Institutes for BioMedical Research, Cambridge, MA as Cheminformatics
Summer Science Intern during summer 2005.
SKILLS
Cheminformatics
Molecular Modeling
Languages
Databases
Platforms
Web Technologies
Tools & Utilities
Others
: Pipeline Pilot, Spotfire, ISIS/Base, ChemDraw, BCI Toolkit
: Spartan, SYBYL, Macro Model, Maestro
: C, JAVA, Visual Basic 6.0
: Oracle 9i, MS-Access
: MS Win NT/9x/2K/XP, UNIX, Linux
: HTML, XML, VBScript, ASP3.0, JSP, Servlets
: Crystal Reports, Matlab
: Labware
EDUCATIONAL EXPERIENCE



MS - Chemical Informatics, Indiana University Purdue University Indianapolis, Indiana.
August 2004 – Till date. Expected graduation December 2005
Masters in Computer Application (M.C.A.), Maharishi Dayanand University, Haryana, India.
September 2003
Bachelor of Science (B.Sc.)-Chemistry (Honors), Delhi University, New Delhi, India.
July 1999
WORK EXPERIENCE
Cheminformatics Summer Science Intern: June 2005 – August 2005 - Novartis Institutes
for BioMedical Research, Cambridge, MA
 Worked with Lead Discovery Informatics division (Davies’ group) on Library and Database
Annotation Concept for a project to explore the relationship between chemical structures and
associated biological activities.
 Platform: Pipeline Pilot, Spotfire, ISIS/Base
Software Engineer - March 2004 – July 2004 - Satyam Computer & Services Ltd.,
Hyderabad, India
 Provided offshore client support to Cisco Systems for development of an application that
incorporated both Client/Server and n-tier Web Architecture
 Worked on J2EE platform using Struts framework
 Documented Use Cases, Test Cases and Design Elements following Satyam’s ISO 9001
quality procedures
 Successfully completed corporate training modules in OOAD & UML, Java, XML and
Quality Assurance
Project Intern - January 2003 – June 2003 - Kirloskar Pneumatic Company Ltd., New
Delhi, India
 Involved in development of an Intranet application which automated the Tender Quotation
Process of the organization
 Performed system analysis & design, development and documentation of the system
 Programming Platform: ASP3.0, VBScript, MS Access 2000
Summer Intern - June 1, 2001 – July 30, 2001. Claas India Ltd., Faridabad, India
 Involved in the development of a desktop application to manage fixed assets of the
corporation.
 Designed and developed the application single handedly.
 Programming Platform: VB6.0, Oracle 9i and Crystal Reports 8.0
RELEVANT TRAINING/WORKSHOPS


Pipeline Pilot Basic Training Course by SciTegic - June 2005.
NCBI workshop on MapViewer – November 2005
AWARDS/FELLOWSHIPS
- MDL Elsevier Excellence in Informatics Fellowship for the year 2004
ACDEMIC PROJECTS & PRESENTATIONS





Conformational search on sixteen mer of a polymeric material that inhibits HIV-1 reverse
transcriptase - Independent study project Fall 2005.
Proteomics Laboratory Study and Implementation of workflow on Labware - Class project
for Lab Info Management Systems course done in Spring 2005.
Tracking system for unsigned Physician Orders - Platform JSP/Oracle 8i - Class project for
Informatics Management course done in Spring 2005.
Evaluation of BCI Toolkit - Class project for Chemical Information Technology course done
in Fall 2004.
Quest for Biomarkers - Class project for Introduction to Bioinformatics course done in Fall
2004.
DETAILS OF COURSEWORK TAKEN
MS in Chemical Informatics
1) Lab Info Management Systems
2) Introduction to Informatics
3) Chemical Information Technology
4) Introduction to Bioinformatics
5) Molecular Modeling
6) Informatics Management
7) Informatics Research & Design
Master’s Program in Computer Application
(A three-year graduate program)
1) Mathematical foundation of computer science
2) Probability and statistics
3) Computer based numerical & statistical techniques
4) Computer based optimization techniques
5) Computer programming & problem solving
6) Business data processing
7) Combination & graph theory
8) Data & file structure
9) System software and C programming
10) Database management system
11) System analysis & design
12) Operating systems
13) Interactive computer graphics
14) Distributed computer network application
15) Simulation & modeling
16) Artificial intelligence
17) Software engineering
18) Computer organization & assembly language
19) Microprocessor & application
20) Computer architecture
21) Accounts & financial management
22) Organizational structure & personnel management
23) Information system design & implementation
Bachelor of Science (with honors in chemistry)
(Not a complete list)
1) Inorganic Chemistry
2) Organic Chemistry
3) Physical Chemistry
4) Environmental Chemistry
5) Mathematics
6) Physics