C2D Cheminformatics : Methods,Tools and Results

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

2. 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.

3. 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...

4. Steps in drug development

5. Cheminformatics in drug design Virtual Screening Data Target Hit Identification Data Mining Building computational models for drug discovery process. Lead identification Lead optimization

6. 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.

7. 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

8. 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

9. 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

10. 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

11. Descriptors Used For Classification

12. 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.

13. 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.

14. Data Mining Taxonomy

15. CLASSIFIER ALGORITHMS IS USED • Bayes classifier Naïve bayes. • Trees j48 Random forest • Functions SMO

16. WORKFLOW

17. 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

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

19. 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.

20. 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.

21. Pre-requisites • Requires .NET framework. Limitation • Windows based

22. 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.

23. Weka – on GARUDA

24. 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

25. Case Study: AID899 To get trained in using different classifiers in weka and analyzing the results

26. Cyp450 - a novel target against Mycobacterium tuberculosis

27. 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

28. 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

29. 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

30. 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

31. Problem Faced Data Redundancy Computational Power Communication – need alternative to SKYPE Institutional limitations – Ban of media stream, social network, chatting, etc.

32. 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.

33. 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  

34. 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.  

35. List of  Best classifiers :  Fp<20,  Accuracy >75

36. sincere thanks to OSDD