Meeting

1. Meeting 26th September, 2013

2. What are drugs? • Organic small molecules • Bind to bio-molecular targets (usually refer to proteins) to activate/inhibittheir functions. • Drugs often affect multiple targets. Target (Protein) Drug (Compound) (Ligand) Image credit: http://www.cs.umd.edu/~shobeir/slides/fakhraei_biokdd_2013.pdf

3. Drug Discovery Image credit: http://www.cs.umd.edu/~shobeir/slides/fakhraei_biokdd_2013.pdf

4. Drug Discovery

5. Drug Discovery • The identification of interactions between drugs (compounds) and targets (proteins) is vitally important in genomic drug discovery. • There are a lot of previous works to identify drug-target interactions but our knowledge about their relationship is still very limited. • E.g. PubChemstores information on millions (106) of chemical compounds but the number of compounds with information on their target proteins is limited.

6. KEGG Drug Database This drug has only one recorded target… http://www.genome.jp/dbget-bin/www_bget?dr:D05606

7. Existing Approaches • Experimental determination of drug-target interactions remain difficult and inefficient. • Computational approach is needed, which are • Docking-based 3D simulations • It cannot be applied to proteins without 3D structures. • It cannot be applied on a large scale as it requires intensive computations. • Text mining (Keyword search) • It cannot be applied to identify new discoveries. • It suffers from the problem of redundancy in the compound/gene/protein names in the literature.

8. Kyoto’s proposed solution (2008)

9. Kyoto’s proposed solution (2008) • The authors proposed a supervised learning approach to predict unknown drug-target interaction. • Based on known drugs, known targets, known drug-target interactions, the authors proposed to apply machine learning techniques to make predictions on unknown data.

10. Agenda • Introduction • Definition and Problem Formulation • Suggested Methods • Nearest profile method • Weighted profile method • Bipartite graph learning method • Experiments and Results • Discussion and Conclusion

11. Definition • A set of known drugs. . • A set of known target proteins. . • The interaction profile of with target proteins is defined as a bit (0/1) string in size of . • The interaction profile of with drugs is defined as a bit (0/1) string in size of .

12. Example • We have a set of drugs:{c1, c2, c3} , then nc = 3. • We have a set of targets: {g1, g2}, then ng = 2. • For each drug ci, we have an interaction profile of size ng = 2, to indicate their interactions with targets. • For each target gj, we have an interaction profile of size nc= 3, to indicate their interactions with drugs.

13. Problem Statement • Suppose that we have a set of known drugs and target proteins and their interaction profiles and , . • Given a new drug candidate compound and a new target candidate protein , predict the corresponding interaction profiles and .

14. Problem Formulation • Training • A set of drugs and its interaction profiles, i.e. • A set of known target proteins, i.e. its interaction profiles, i.e. . • Input • New drug, • New protein, • Output • Interaction profile, • Interactin profile, .

15. Suggested Methods • Nearest profile method • Weighted profile method • Bipartite graph learning method

16. Nearest profile method • Predict the new drug , to have the following interaction profile: ,where is a chemical similarity score ,and is the nearest compound to . e.g. 0.86… e.g. [1,0,…] 0.86 [1,0,…]

17. Nearest profile method • Predict the new protein , to have the following interaction profile: ,where is a sequence similarity score ,and is the nearest protein to . 0.86 e.g. 0.86… e.g. [1,0,…]

18. Nearest profile method • Finally, we predict if and will interact: e.g. 0.4 e.g. 0.7 A threshold , can be used to decide if there is an interaction between a drug-target pair. Obtained from Step 1 0.5 Obtained from Step 2 0.4 0.7 0.8

19. Suggested Methods • Nearest profile method • Weighted profile method • Bipartite graph learning method

20. Weighted profile method • Predict the new drug , to have the following interaction profile: ,where is a chemical similarity score ,and e.g. 0.4 0.3 0.8

21. Weighted profile method • Predict the new target protein , to have the following interaction profile: ,where is a sequence similarity score ,and 0.4 0.3 e.g. 0.8

22. Weighted profile method • Finally, we predict if and will interact: A threshold , can be used to decide if there is an interaction between a drug-target pair. 0.1 0.2 e.g. 0.3 0.4 0.3 0.1 0.8 0.2 0.5 e.g. 0.3 0.6 0.7

23. Suggested Methods • Nearest profile method • Weighted profile method • Bipartite graph learning method

24. Bipartite graph learning method • The proposed procedure is as follows: • Embed drugs and targets on the interaction network into a unified space which is called “pharmacological space”. • Learn a model between the chemical/genomic space and pharmacological space, such that we can map any drugs/targets onto the “pharmacological space”. • Predict interacting pairs by connecting drugs and targets which are closer than a threshold in the “pharmacological space”.

25. Bipartite graph learning method Get its feature (coordinate) Get its feature (coordinate) Figure 1. An illustration of the proposed method

26. Bipartite graph learning method • Step 0: Understanding the Objective • A drug-target interaction network is described by a bipartite graph: G= (V1+V2, E), where V1 is a set of drugs, V2 is a set of targets and E is a set of interactions. • Objective: To represent the bipartite graph in Euclidian space such that both drugs and targets are represented by sets of q-dimensional feature vectors.

27. Bipartite graph learning method • Step 1: Construct a graph-based similarity matrix, • , j = ,where d is the shortest distance between objects on the graph, where unreachable object pairs is set as infinity, and h is a width parameter set by user. d:2

28. Bipartite graph learning method • Step 2: Obtain the features of the drugs and targets in the “pharmacological space”. • Apply an eigenvalue decomposition on K, , where the diagonal elements of is eigenvalues and columns of are eigenvectors, and U = . • The features of all drugs and targets are represented by row vectors of such that

29. Bipartite graph learning method • Step 3: Learn a regression model to connect the chemical space/genomic space to the pharmacological space. • Here, we aim to learn a set of weights to minimize the error terms: • For drugs, • Minimize: • For targets, • Minimize: :

30. Bipartite graph learning method • Step 4: Map the new drug/protein on the pharmacological space

31. Bipartite graph learning method • Step 5: Predict drug-target interaction by closeness. • Compute • Compute • Compute If any of these values are less than a threshold t, we predict there is an interaction between the pair.

32. Datasets The information about the interactions between drugs and target proteins from the KEGG BRITE (Kanehisa et al., 2006), BRENDA (Schomburg et al., 2004), SuperTarget (Gunther et al., 2008) and DrugBank databases (Wishart et al., 2008).

33. Experiments • Compare “Nearest profiles”, “Weighted profiles” and “Bipartite graph learning” under 10-fold cross-validation on 4 classes of drug-target interactions: • Enzymes • Ion channels • GPCRs • Nuclear receptors

34. Prediction Performance Sensitivity = ; Specificity = ; PPV =

35. ROC of Bipartite graph learning True positive rate = ; False Positive Rate =

36. ROC of Bipartite graph learning True positive rate = ; False Positive Rate =

37. Predicted drug-target interaction network of the “Enzyme” dataset Blue node: known drugs Red node: known targets Light blue node: newly predicted compounds Orange node: newly predicted proteins Gray edge: known interactions Pink edge: newly predicted interactions

38. Potential New Discoveries

39. Discussion and Conclusion • The authors have developed new machine learning methods to predict unknown drug-target interaction. • The originality of the method lies in • The formation of the drug-target interaction as a supervised learning problem for a bipartite graph. • The lack of need for 3D structural information of the target proteins. • The integration of chemical and genomic spaces into a unified space that is called “Pharmacological space”.

40. Discussion and Conclusion • A key observation is that two drugs sharing high structural similarity tend to interact with similar target proteins. • Likewise, two proteins sharing a high similarity tend to interact with similar drugs. • However, there were some exceptional examples where this tendency was weak. This explains why nearest profile methods and weighed profile methods failed. • In contrast, the graph learning method is able to correct such bias, which is made possible by learning a model based on network topology.

41. Discussion and Conclusion • To their knowledge, this is the first study to predict bipartite graphs in a supervised context. • Limitation: No/Few negative examples. • Possible future work includes • Use sophisticated similarity functions. • Use sophisticated machine learning models. • Incorporate functional sites into the protein similarity design.

42. A new trend: Drug Repurposing • Drug Repurposing: Finding new uses ofapproved drugs. An example: Sildenafil Citrate -> Viagra Sildenafil Citrate is originally developed for pulmonary arterial hypertension. It is now used for treatment of erectile dysfunction.

43. Why Drug Repurposing? • Repurposed drugs offer the advantages of • Lower failure rate Safety accounts for 30% of drug failures in clinical trials1. • Money-saving $1.3 B to develop a new drug; $8.4 M to re-launch an existing drug1. • Faster time to market A reduction of R&D timelines by 3-5 years2. http://www.ddw-online.com/business/p142737-the%20benefits%20of%20drug%20repositioning.%20%20spring%2011.html http://thomsonreuters.com/business-unit/science/subsector/pdf/knowledge-based-drug-repositioning-to-drive-rd-productivity.pdf

44. Take home message: Supervised Bipartite Graph Learning method B A A B A B 160 citations since 2008