Presentation covers all aspects of drug discovery which are greatly useful for pharmaceutical and drug discovery companies.

1. Improving Research Productivity www.vlifesciences.com

2. Acknowledgements All trademarks, methodologies, product names mentioned in this presentation are sole property of their respective owners.

4. Section – I

5. VLife technologies for Early & Late stage discovery research

6. Section – II

7. Innovations from VLife

8. Section – III

9. Technology benchmarking for VLife’s innovations

10. Section – IV

12. Strategic investment by TATA group

13. Strong in-house team for innovation

14. Scientists with 300+ man-years of drug discovery research experience

15. Software developers with strong algorithm expertise

17. 100% ownership of code

18. Modular & componentized

19. License just what you need

20. Build on as needs grow

21. High level of plug-in / plug-out

22. Proven innovation ability

23. Patented GQSAR technology for lead optimization

24. VLifeSCOPE technology for lead optimization & prioritization

26. NewEdge platform: Capabilities Starting point:Molecular libraries Hit identification: Virtual screening on shape / fingerprint/ pharmacophore / Docking Early stage discovery The NewEdge Platform Molecular Modeling & Simulation Hit filtration: Screening based on target specificity / QSAR based screening if relevant data is available Hit to Lead: Fragment based approach / Scaffold hopping based approach Library generation: Clues for fragment replacement / Clues for growth / Hybrid library Late stage discovery Lead optimization: Site directed optimization clues / Residue contribution towards Binding energy End point:Selected candidate with high reliability

27. Hit identification technologies

28. Hit filtration technologies Filtration of identified hits using specificity criteria narrows down the list. VLife’s NewEdge platform offers multiple hit filtration methods based on the similar principal.

29. Technologies for ‘Hit to Lead’ Filtered hits require deeper understanding of chemical space requirements and would require clues for scaffold hopping to achieve novelty. VLife’s NewEdge platform offers multiple tools for obtaining novel Leads from the hits.

30. Technologies for Library generation The identified lead is utilized further for exploration of chemical space by generating lead-like library. VLife’s NewEdge platform offers multiple library generation tools.

31. Novel technologies for Lead optimization

32. Protein structure analysis Active site analysis Homology modeling NewEdge: End-to-end capabilities Property visualization Docking QSAR analysis Database querying Virtual screening Multiple scenarios single platform NewEdge platform: Application summary Approaches Applications Activity data I Yes Primary lead chemistry No activity data II Yes Pharmacophore identification No III Yes IV Conformer generation Target structure Primary lead chemistry Close homolog Combinatorial library V No No VI Activity data Remote homolog No activity data VII

33. Section – II Innovations from VLife www.vlifesciences.com

35. Property mapped region is necessary for specificity

41. Partitioning of binding energy or docking score in to residue wise interactions terms & utilized as descriptors, f(Exp. Activity)

43. Improved ranking of ligands compared to docking

47. QSAR benchmarking II: kNN MFA Activity prediction benchmarking : VLifeSCOPE Comparison of VLifeSCOPE with force field based docking as a means of predicting likely experimental MIC Accuracy measure:Rank order comparison of each molecule of the data set with their MIC Reference: Modeling and interactions of Aspergillusfumigatuslanosterol 14-α demethylase ‘A’ with azole anti fungals (Bioorganic & Medicinal Chemistry 2004, 12 2937–2950) With VLife SCOPE predicted rank order for first four compounds exactly matches experimental finding while binding energy based rank order is completely off track.

48. QSAR benchmarking I: GQSAR QSAR benchmarking : GQSAR Comparison of patent pending GQSAR with other 2D QSAR and 3D QSAR methods for accuracy of predicted activity Accuracy measure: Established statistical measures, pred_r2 and q2 Reference: Group-Based QSAR (G-QSAR): Mitigating Interpretation Challenges in QSAR ,Subhash Ajmani, Kamalakar Jadhav, Sudhir A. Kulkarni, QSAR & Combinatorial Science, 28, 1, 2009, 36–51 VLife’s patented GQSAR is more accurate than similar technologies and far more insightful for lead optimization.

49. QSAR benchmarking II: kNN MFA QSAR benchmarking : kNN-MFA Comparison of kNN MFA method with other QSAR methods for accuracy of prediction in case of non-linear relationships Accuracy measure:Established statistical measures, pred_r2 and q2 Steroids Cancer Anti-Inflammatory Pred_r2 q2 Pred_r2 q2 Pred_r2 q2 Reference: Three-Dimensional QSAR Using the k-Nearest Neighbor Method and Its Interpretation by Subhash Ajmani, Kamalakar Jadhav, Sudhir A. Kulkarni , Journal of Chemical Information and Modeling, 2006, 46, 24-31 VLife’s kNN-MFA method is consistently more accurate than similar technologies across widely varying chemistries.

50. Accuracy Docking benchmarking I: GRIP Docking benchmarking – I: GRIP Comparison with multiple other technologies for accuracy Accuracy measure:Difference of < 1A0 between predicted and laboratory determined result Reference: Standard data for comparison taken from ‘Deciphering common failures in molecular docking of ligand-protein complexes’ by G.M. Verkhivker, D. Bouzida, D.K. Gehlhaar, P.A. Rejto, S. Arthurs, A.B. Colson, S.T. Freer, V. Larson, B. A. Luty, T. Marronne, P.W. Rose, J. Comp. Aid. Mol. Des., 2000, 14, 731-751

51. Docking benchmarking II: GRIP Docking benchmarking – II: GRIP Comparison with multiple other technologies for speed and ability to handle complex molecules Speed measure:Minutes taken per docking Molecular complexity measure:Number of rotatable bonds within molecule Complexity Speed VLife’s GRIP docking is faster, more accurate and is better able to handle complex molecules vis-a-vis wide spectrum of competing technologies.

53. Journal of Medicinal Chemistry

54. Protein Science

55. Molecular BioSystems

56. BMC Bioinformatics

57. Journal of Computer-Aided Molecular Design

58. Journal of Molecular Modeling

59. Molecular Simulation

60. Bioorganic & Medicinal Chemistry

61. Journal of Chemical Information and Modeling

62. European Journal of Medicinal Chemistry

63. Molecular Diversity

64. QSAR & Combinatorial Science

65. Journal of Molecular Graphics and Modeling

66. Journal of Enzyme Inhibition and Medicinal Chemistry

67. Section – IV Post discovery strategic research services www.vlifesciences.com

68. Multiple scenarios single platform VLifeRVHTS platform: Disclosable approach T3 Target Knowledgebase Target Specific Compound Knowledgebase Target knowledgebase covers database of 1066 targets Knowledgebase covers all possible co-crystallized (CC) and known compounds(KC) with reported activity for a respective protein target T2 T1066 T1 T1 T3 T2 T1066 Screening Lead Compound binding with IRBS Intelligent Rule Based System (IRBS) Derived from VLife RVHTS Platform KC1? CC1? KC2? KC3? CC3? KC1060? CC1066? CC2? Target Selection VLifeIRBS Filtered out with VLifeRVHTS An Intelligent Rule Based System is based on binding study of target specific compound database and Target knowledgebase Selected Targets with VLifeRVHTS Interaction Studies New putative targets for the query compound Priorities and suggestions

70. Identifying novel therapeutic potential of customer’s molecule in non-GPCR targets

71. VLife’s challenge and approach

72. Identification of putative targets with which molecule interacts and suggesting priority to customer for experimental validation

73. VLifeRVHTS was used and priorities decided using VLifeIRBS

74. Results and Validation

75. Customer provided with 5 putative targets for experimental validation

76. Two targets out of three showed expected off target activity

77. Status of discovery

79. Comparing potential of customer’s NCE vis-a-vis existing drugs for selected set of GPCR targets

80. VLife’s challenge and approach

82. Results and Interpretation

83. Detailed comparison was provided for all 22 targets

84. Validation Experiment: 19 out of 22 correct

86. Find revival opportunities for the withdrawn drug and validate it

87. VLife’s challenge and approach

88. VLifeRVHTS was used to perform docking in all 1066 targets

89. Hits were prioritized on the basis of comparative binding energy of known ligands for the target using VLifeIRBS

90. Results and Interpretation

93. Predict toxicity profile & adverse events of a customer’s compound

94. VLife’s challenge and approach

95. In-house models used for predicting various toxicity endpoints

96. Using VLifeRVHTS, compound docked in targets associated with adverse events

97. Results and Interpretation