Post on 14-Dec-2015
C2D Cheminformatics : Methods,Tools and Results
ByOSDD-Cheminformatics team
The burden of TB
• About 9 million people were infected with TB in year 2009, and 1.7 million died
• India is the world Tb capital with estimated 1.9 million cases reported every year.
• India has 2nd largest estimated number of MDR-TB cases(99000 in 2008).
• By July 2010, 58 countries had reported at least 1 case of XDR-TB.
Cheminformatics : What?• COMPUTERS have been applied to solve problems
almost everywhere. When we use them in chemistry, we call it cheminformatics.
• Cheminformatics is applied mostly to large number of molecules.
• Deals with – Storage, retrieval and crosslinking of chemical structures
and associated data.– Prediction of physical, chemical and biological properties
of compounds.– Analysis and prediction of reactions.– Drug Design...
Steps in drug development
Cheminformatics in drug design
Target Virtual Screening Data
Data MiningHitIdentification
Lead identification
Building computational
models for drug discovery
process.
Lead optimization
Aim of Cheminformatics Project
• To screen molecules interacting with the Potential TB targets using classifiers.
• Select the selected molecules and dock with Targets to further screen the molecules for leads.
• Use cheminformatics techniques such as QSAR ,3D QSAR, ADMET to look for potential leads and design Drugs using the leads – by building combinatorial libraries.
Ways to perform Virtual screening
• Use a previously derived mathematical model that predicts the biological activity of each structure
• Run substructure queries to eliminate molecules with undesirable functionality
• Use a docking program to identify structures predicted to bind strongly to the active site of a protein (if target structure is known)
• Filters remove structures not wanted in a succession of screening methods
Main Classes of Virtual Screening Methods
• Depend on the amount of structural and bioactivity data available– One active molecule known: perform similarity search
(ligand-based virtual screening)– Several active molecules known: try to identify a common
3D pharmacophore, then do a 3D database search– Reasonable number of active and inactive structures
known: train a machine learning technique (with the help of Molecular descriptors or Molecular properties)
– 3D structure of the protein known: use protein-ligand docking
Molecule PropertiesSPC : Structure Property CorrelationSPC : Structure Property Correlation
INTRINSIC INTRINSIC PROPERTIESPROPERTIES Molar Volume Connectivity Indices Charge Distribution Molecular WeightPolar surface Area
INTRINSIC INTRINSIC PROPERTIESPROPERTIES Molar Volume Connectivity Indices Charge Distribution Molecular WeightPolar surface Area
CHEMICAL PROPERTIESCHEMICAL PROPERTIESpKaLog PSolubilityStability
CHEMICAL PROPERTIESCHEMICAL PROPERTIESpKaLog PSolubilityStability
BIOLOGICAL PROPERTIESBIOLOGICAL PROPERTIESActivityToxicityBiotransformationPharmacokinetics
BIOLOGICAL PROPERTIESBIOLOGICAL PROPERTIESActivityToxicityBiotransformationPharmacokinetics
Molecular descriptors used for machine Learning
Molecular descriptors are numerical values that characterize properties of molecules.
The descriptors fall into Four classes a) Topological b) Geometrical c) Electronic d) Hybrid or 3D Descriptors
Descriptors Used For Classification
Name of Descriptors used
Number of Descriptors
Pharmacophore Fingerprints
147
Weighted Burden Number
24
Properties 8
Data mining
According to David Hand et al., of MIT press (2001)“ Data mining is the analysis of (often large) observational data sets to find
unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner”.
Data mining …. But why? Data Information Knowledge
The main aim of a user is always to extract knowledge from an information obtained from data.
Data mining is one of key step in Knowledge discovery process, although sometimes it is confused with Knowledge discovery itself!
A user always looks for more information search with least amount of time being spent on exploring the resources.
Data mining in Cheminformatics
• Data mining approaches are an integral part of cheminformatics and pharmaceutical research.
• This will tend to increase due to the increase of computational methods for biology and chemistry.
• Data mining has found major use in the virtual screening process of cheminformatics.
Data Mining Taxonomy
CLASSIFIER ALGORITHMS IS USED
• Bayes classifier Naïve bayes.
• Trees j48
Random forest• Functions SMO
WORKFLOW
Accessing the HTS bioassay
data
Upload the sdf file
All compounds
sdf file
Generate descriptor file
Open the CSV file in Excel
Bioassay result (all)
Testing
Training File splitting
Remove the useless attributes
Select the actives and inactive compounds
Apply classifier
algorithms
Selection of best classifier
model
TP %, FP<20%, Accuracy >70%
Append the bioassay result corresponding
to the compounds
PubChem
PowerMV PowerMV
Excel
WEKA
Molecular Descriptor generation
• Chemistry Development Kit (CDK) – http://rguha.net/code/java/cdkdesc.html
• PowerMVhttp://nisla05.niss.org/PowerMV/?q=PowerMV
PowerMv• A Software Environment for Molecular Viewing, Descriptor
Generation, Data Analysis and Hit Evaluation.
• An operating environment for biologists and statisticians for viewing or browsing medium to large molecular SD files, computing descriptors.
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Features
• Importing, viewing and sorting SD files.
• Capacity is limited only by available memory.
• Compounds structure and attributes can be easily exported to Microsoft Excel.
Pre-requisites• Requires .NET framework.
Limitation• Windows based
Weka - toolkit• Collection of machine learning algorithms for data analysis
and classification experiments.
• Tools available for data pre-processing, classification, regression, clustering, association rules, and visualization.
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Weka – on GARUDA
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The Script file• RemoveUselessAttributes
java <CLASSPATH> -Xmx4000m weka.filters.unsupervised.attribute.RemoveUseless -i <in.csv> -o <out.csv>
• Using cost-sensitive classification
java <CLASSPATH> –Xmx4000m weka.classifiers.meta.CostSensitiveClassifier -cost-matrix “[0.0 10.0; 1.0 0.0]” -t AID1626train.arff -x 5 -d smo.model -W weka.classifiers.functions.SMO -i -- -M
Case Study: AID899
To get trained in using different classifiers in weka and analyzing the
results
Cyp450 - a novel target against Mycobacterium tuberculosis
The P450s are mono-oxygenase enzymes,
Generally interact with flavoprotein and/or iron–sulphur centre redox partners for catalysis
The Mtb genome sequence—a plethora of P450s .
‘‘P450 dense’’ by comparison with eukaryotic genomes
•most effective azoles have extremely tight binding constants for one of the Mtb P450s (CYP121).
Thus, analysis of Mtb CYP51 revealed P420 is an irreversibly inactivated and structurally disrupted species.
Organism P450s Genome size Ratio
Humans 57 3.3 billion bp 1:5.8 million bp
D. melanogaster 84 123 million bp 1: 1.5 million bp
A. thaliana has 249 115 million bp 1: 462,000 bp
M. tuberculosis 20 4.4 million bp 1: 220,000 bp
Mutations were largely located not in the active site area itself, but instead in regions that are conformationally mobile, where entry and exit of substrate to the active site is facilitated
Thus, acquired resistance could be mediated by mutations and it enhances flexibility and conformational rearrangements to increased activity
Why Cyp450
Objectives
To develop model from AID 899 HTS to study the compound/drug interaction with Human CYP450.
Why1) A lead molecule developed should not interact with CYP450 of
human a) Drug metabolism b) affecting CYP450
2) It should work against CYP450 of M.tuberculosis
Work plan
Select active/inactive compounds against human CYP450 from Pubchem HTS data Generate model for lead compound screening Screen the compounds via model Select the inactives
Go for testing against mycobacterium CYP450 (model) Select active lead compound Go for insilico drug designing Invitro studies and invivo studies
Current working
To be worked
Confusion Matrix
TPActive classified as active
FNActive classified as
inactive
FPInactive classified as
active
TNInactive classified as inactive
Base Classifier and Cost Sensitive Classifier (CSC)
CSC setting cost factor False Negative TP, FP rate increases
So FN is important than FP
Problem Faced
Data Redundancy
Computational Power
Communication – need alternative to SKYPE
Institutional limitations – Ban of media stream,
social network, chatting, etc.
Data Redundancy
Tried two approaches for processing the AID to obtain train and test data set.
Method 1: We downloaded sdf file containing all tested compounds. We downloaded bioassay data files for the same. Then we matched it in MS excel. It contained active, inactive, inconclusive and discrepancy We further selected only active and inactive and ran in PowerMV to get csv Then after converting to arff we processed test and train from it. Loaded the two files in Weka and used different algorithms to build best model.
Method 2: We download active and inactive SDF files separately from the same pubchem page. After processing in PowerMV both files were combined to form one. Then similar steps were followed as in Method 1.
Problem: The number of final active and inactive compounds differ between the methods.
Active Inactive Discrepancy Inconclusive
Method I 1767 6255 230 1127
Method II 1901 6441 Nil 1279
AID 899 - not curated “Problem reported to pubchem“. Director will be looking at it.
Progress & Results
1) We understood the basic working with weka
2) How to derive results from confusion matrix
3) Ignored Classifier gives good results (LAZY)
4) Got good results with RANDOM FOREST, etc unlike reported in Virtual bioassay paper
5) Maximum accuracy of 86.16
Strategy followed
From the preliminary investigation it is clear that AID 899 is not a properly curated dataset In method I many classifiers were applied and the results are represented below In method II still many classifiers can be run and results generated.
List of Best classifiers : Fp<20, Accuracy >75
sincere thanks to
OSDD