Samuel Lunenfeld Research Institute, Mt. Sinai Hospital, Toronto, Canada, July 25, 2008

1. Making the best of a bad situation Using side effects to reveal new targets for old drugs Lars Juhl Jensen EMBL Heidelberg

2. the problem

3. two drugs

4. do they share a target?

5. chemical similarity

6. Tanimoto coefficients

9. similar drugs share targets

10. only trivial predictions

11. the idea

12. chemical perturbations

13. phenotypic readout

14. drug treatment

15. side effects

16. the implementation

17. information on side effects

18. package inserts

20. text mining

21. side-effect ontology

22. backtracking

24. side-effect correlations

26. GSC weighting

27. side-effect frequencies

29. raw similarity score

31. p-values

33. side-effect similarity

34. chemical similarity

36. reference set

37. drug–target pairs

39. drug–drug pairs

40. score bins

41. benchmark

43. fit calibration function

45. probabilistic scores

46. the results

47. drug–drug network

49. categorization

54. map onto score space

56. the experiments

57. 20 drug–drug relations

58. in vitro binding assays

62. K i <10 µM for 11 of 20

63. cell assays

64. 9 of 9 showed activity

66. the future

67. drug–target relations

70. http://larsjuhljensen.wordpress.com