1. Introduction to Chemoinformatics and Drug Discovery Irene Kouskoumvekaki Associate Professor February 15 th, 2013

2. 2CBS, Department of Systems Biology The Chemical Space There are atoms and space. Everything else is opinion. —Democritus (ca. 460 BC – ca. 370 BC)

3. 3CBS, Department of Systems Biology Systems Chemical Biology

4. 4CBS, Department of Systems Biology Today’s Learning Objectives To introduce you to the field of chemoinformatics and the most commonly used terms and methods To show examples of the use of chemoinformatics in modern drug research To give you practical experience through hands-on exercises

5. 5CBS, Department of Systems Biology Drug Discovery Process Disease Drug target Drug candidate Animal studies Clinical studies Marketed drug

6. 6CBS, Department of Systems Biology A drug candidate… ... is a (ligand) compound that binds to a biological target (protein, enzyme, receptor,...) and in this way either initiates a process (agonist) or inhibits it (antagonist/inhibitor)  The structure/conformation of the ligand is complementary to the space defined by the protein’s active site  The binding is caused by favorable interactions between the ligand and the side chains of the amino acids in the active site. (electrostatic interactions, hydrogen bonds, hydrophobic contacts...)

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8. 8 Wet-lab drug discovery process Screening collection HTS Actives 10 3 actives10 6 cmp.

9. 9CBS, Department of Systems Biology Screening collection HTS Actives 10 3 actives10 6 cmp. High rate of false actives!!! High throughput is not enough to get high output….. Wet-lab drug discovery process

10. 10CBS, Department of Systems Biology Screening collection HTS Actives 10 3 actives10 6 cmp. Follow-up Chemical structure Purity Mechanism Activity value Wet-lab drug discovery process

11. 11CBS, Department of Systems Biology Screening collection HTS Actives 10 3 actives10 6 cmp. Follow-up Hits 1-10 hits Analogues synthesis and testing ADMET properties Wet-lab drug discovery process

12. 12CBS, Department of Systems Biology Wet-lab drug discovery process Screening collection HTS Actives 10 3 actives10 6 cmp. Follow-up Hits 1-10 hits Lead series 0-3 lead series Hit-to-lead Analogues synthesis and testing ADMET properties

13. 13CBS, Department of Systems Biology Wet-lab drug discovery process Screening collection HTS Actives 10 3 actives10 6 cmp. Follow-up Hits 1-10 hits Lead series 0-3 lead series Hit-to-lead Drug candidate 0-1 Lead-to-drug Analogues synthesis and testing ADMET properties

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15. 15CBS, Department of Systems Biology Failures

16. 16CBS, Department of Systems Biology We need more.. to find less..

17. 17CBS, Department of Systems Biology Drug Discovery Process Disease Drug target Drug candidate Animal studies Clinical studies Marketed drug Chemoinformatics

18. 18CBS, Department of Systems Biology Wet-lab + Dry-lab drug discovery Diverse set of molecules tested in the lab in vitro in silico + in vitro Computational methods to select subsets (to be tested in the lab) based on prediction of drug-likeness, solubility, binding, pharmacokinetics, toxicity, side effects,...

19. 19CBS, Department of Systems Biology The Lipinski ‘rule of five’ for drug- likeness prediction  Octanol-water partition coefficient (logP) ≤ 5  Molecular weight ≤ 500  # hydrogen bond acceptors (HBA) ≤ 10  # hydrogen bond donors (HBD) ≤ 5 If two or more of these rules are violated, the compound might have problems with oral bioavailability. (Lipinski et al., Adv. Drug Delivery Rev., 23, 1997, 3.)

20. 20CBS, Department of Systems Biology Exercise : Drug-likeness by ’rule of five’ Go to the following webpage www.molsoft.com/mprop Draw proguanil, calculate properties and decide if this compound is a drug according to ’rule of five’

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22. 22CBS, Department of Systems Biology Proguanil antimalarian tablets

23. 23CBS, Department of Systems Biology Chemoinformatics Gathering and systematic use of chemical information, and application of this information to predict the behavior of unknown compounds in silico. dataprediction

24. 24CBS, Department of Systems Biology Major Aspects of Chemoinformatics Databases: Development of databases for storage and retrieval of small molecule structures and their properties. Machine learning: Training of Decision Trees, Neural Networks, Self Organizing Maps, etc. on molecular data. Predictions: Molecular properties relevant to drugs, virtual screening of chemical libraries, system chemical biology networks…

25. 25CBS, Department of Systems Biology Major Aspects of Chemoinformatics Databases: Development of databases for storage and retrieval of small molecule structures and their properties. Machine learning: Training of Decision Trees, Neural Networks, Self Organizing Maps, etc. on molecular data. Predictions: Molecular properties relevant to drugs, virtual screening of chemical libraries, system chemical biology networks…

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27. 27CBS, Department of Systems Biology Representing a chemical structure How much information do you want to include? –atoms present –connections between atoms bond types –stereochemical configuration –charges –isotopes –3D-coordinates for atoms C 8 H 9 NO 3

28. 28CBS, Department of Systems Biology Representing a chemical structure How much information do you want to include? –atoms present –connections between atoms bond types –stereochemical configuration –charges –isotopes –3D-coordinates for atoms

29. 29CBS, Department of Systems Biology Representing a chemical structure How much information do you want to include? –atoms present –connections between atoms bond types (aromatic ring identification) –stereochemical configuration –charges –isotopes –3D-coordinates for atoms

30. 30CBS, Department of Systems Biology Representing a chemical structure How much information do you want to include? –atoms present –connections between atoms bond types –stereochemical configuration –charges –isotopes –3D-coordinates for atoms

31. 31CBS, Department of Systems Biology Representing a chemical structure How much information do you want to include? –atoms present –connections between atoms bond types –stereochemical configuration –charges –isotopes –3D-coordinates for atoms

32. 32CBS, Department of Systems Biology Representing a chemical structure How much information do you want to include? –atoms present –connections between atoms bond types –stereochemical configuration –charges –isotopes –3D-coordinates for atoms

33. 33CBS, Department of Systems Biology Representing a chemical structure How much information do you want to include? –atoms present –connections between atoms bond types –stereochemical configuration –charges –isotopes –3D-coordinates for atoms

34. 34CBS, Department of Systems Biology From chemists to representations

35. 35CBS, Department of Systems Biology Structural representation of molecules Line notations Connection tables

36. 36CBS, Department of Systems Biology SMILES (Simplified Molecular Input Line Entry System) Canonical SMILES: unique for each structure Isomeric SMILES: describe isotopism, configuration around double bonds and tetrahedral centers, chirality

37. 37CBS, Department of Systems Biology InChI (IUPAC International Chemical Identifier)

38. 38CBS, Department of Systems Biology MOLfile format (.sdf)

39. 39CBS, Department of Systems Biology Small molecule databases

40. 40CBS, Department of Systems Biology Try it yourself! Go to PubChem: pubchem.ncbi.nlm.nih.gov/ Type proguanil and press Go Click on the first result on the list

41. 41CBS, Department of Systems Biology Try it yourself! Scroll down and find the SMILES and InChI

42. 42CBS, Department of Systems Biology Try it yourself! Click on SDF (top right icon) Select: 2D SDF: Display

43. 43CBS, Department of Systems Biology Try it yourself! Go back and click again on SDF Select: 3D SDF: Display

44. 44CBS, Department of Systems Biology Questions?

45. 45CBS, Department of Systems Biology