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C2D Cheminformatics : Methods,Tools and Results. By OSDD-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.

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the burden of tb
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
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...
cheminformatics in drug design
Cheminformatics in drug design

Virtual Screening Data

Target

Hit

Identification

Data Mining

Building computational models for drug discovery process.

Lead identification

Lead optimization

aim of cheminformatics project
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
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
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 properties
Molecule Properties

CHEMICAL PROPERTIES

pKa

Log P

Solubility

Stability

INTRINSIC PROPERTIES

Molar Volume

Connectivity Indices

Charge Distribution

Molecular Weight

Polar surface Area

BIOLOGICAL PROPERTIES

Activity

Toxicity

Biotransformation

Pharmacokinetics

SPC : Structure Property Correlation

molecular descriptors used for machine learning
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

data mining
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 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.
classifier algorithms is used
CLASSIFIER ALGORITHMS IS USED
  • Bayes classifier

Naïve bayes.

  • Trees

j48

Random forest

  • Functions

SMO

slide17

Accessing the HTS bioassay data

PubChem

PowerMV

PowerMV

All compounds sdf file

Upload the sdf file

Generate descriptor file

Open the CSV file in Excel

Append the bioassay result corresponding to the compounds

Excel

Bioassay result (all)

Select the actives and inactive compounds

Remove the useless attributes

TP %, FP<20%, Accuracy >70%

Apply classifier algorithms

File splitting

Training

WEKA

Selection of best classifier model

Testing

slide18

Molecular Descriptor generation

  • Chemistry Development Kit (CDK) –
  • http://rguha.net/code/java/cdkdesc.html
  • PowerMV
  • http://nisla05.niss.org/PowerMV/?q=PowerMV
slide19

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.
slide20

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.
slide21

Pre-requisites

  • Requires .NET framework.

Limitation

  • Windows based
slide22

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.
slide24

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

Case Study: AID899

To get trained in using different classifiers in weka and analyzing the results

slide27

Why Cyp450

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

slide28
Objectives

To develop model from AID 899 HTS to study the compound/drug interaction with Human CYP450.

Why

  • 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
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
Confusion Matrix

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
Problem Faced

Data Redundancy

Computational Power

Communication – need alternative to SKYPE

Institutional limitations – Ban of media stream,

social network, chatting, etc.

data redundancy
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.

AID 899 -  not curated  “Problem reported to pubchem“. Director will be looking at it.

progress results
Progress & Results
  • We understood the basic working with weka
  • How to derive results from confusion matrix
  • Ignored Classifier gives good results (LAZY)
  • Got good results with RANDOM FOREST, etc unlike reported in Virtual bioassay paper
  • Maximum accuracy of 86.16  
strategy followed
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.