Chemoinformatics in Drug Design

1. Chemoinformatics in Drug Design Irene Kouskoumvekaki, Associate Professor, Computational Chemical Biology, CBS, DTU-Systems Biology Biological Sequence Analysis, May 6, 2011

2. Computational Chemical Biology group Tudor Oprea Guest Professor Olivier Taboureau Associate Professor Irene Kouskoumvekaki Associate Professor Sonny Kim Nielsen PhD student Kasper Jensen PhD student Ulrik Plesner master student

5. Gathering and systematic use of chemical information, and application of this information to predict the behavior of unknown compounds in silico. Definition:Chemoinformatics data prediction

6. Definition: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) • 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 acidsin the active site. (electrostatic interactions, hydrogen bonds, hydrophobic contacts...)

7. Drug Discovery Animal studies In vitro / In silico studies Clinical studies

8. The Drug Discovery Process Chemoinformatics

9. The Drug Discovery Process We identify/predict the binding pocket We know the structure of the biological target MKTAALAPLFFLPSALATTVYLA GDSTMAKNGGGSGTNGWGEYL ASYLSATVVNDAVAGRSAR…(etc) Challenge: To design an organic molecule that would bind strong enough to the biological target and modute it’s activity. New drug candidate

10. Example: – Alzheimer’s disease What is it? Alzheimer's is a disease that causes failure of brain functions and dementia. It starts with bad memory and disability to function in common everyday activities. How do you get it? Alzheimer's disease is the result of malfunctioning neurons at different parts of the brain. This, in turn, is due to an inbalance in the concentration of neurotranmitters.

11. Example: – Alzheimer’s disease How can we treat it? Acetylkolin neurotransmitter Drug against Alzheimer’s

12. Old School Drug discovery process Lead-to-drug HTS Follow-up Hit-to-lead Screening collection Lead series Drug candidate Actives Hits 106 cmp. 103 actives 1-10 hits 0-3 lead series 0-1 Clinical trials High rate of false positives !!!

14. Failures

15. Drug discovery in the 21st Century in vitro in silico+ in vitro Diverse set of molecules tested in the lab Computational methods to select subsets (to be tested in the lab) based on prediction of drug-likeness, solubility, binding, pharmacokinetics, toxicity, side effects, ...

16. 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.)

17. Major Aspects of Chemoinformatics

18. Major Aspects of Chemoinformatics • Information Acquisition and Management: Methods for collecting data (mainly experimental). Development of databases for storage and retrieval of information. • Information Use: Data analysis, correlation and model building. • Information Application: Prediction of molecular properties relevant to chemical and biochemical sciences.

19. Major Aspects of Chemoinformatics • Information Acquisition and Management: Methods for collecting data (mainly experimental). Development of databases for storage and retrieval of information. • Information Use: Data analysis, correlation and model building. • Information Application: Prediction of molecular properties relevant to chemical and biochemical sciences.

20. Information Acquisition and Management

21. Small molecule databases

22. Growth In PubChem Substances & Compounds Recent count: Substance: 72,156,631 Compound: 28,807,320 Rule of 5: 20,692,980

23. Searching in PubChem

24. Structural representation of molecules Structural representation of molecules

25. Major Aspects of Chemoinformatics • Information Acquisition and Management: Methods for collecting data (mainly experimental). Development of databases for storage and retrieval of information. • Information Use: Data analysis, correlation and model building. • Information Application: Prediction of molecular properties relevant to chemical and biochemical sciences.

26. Beyond the Lipinski Rule of 5... • Chemometrics: The application of mathematical or statistical methods to chemical data (simple, linear methods) e.g. Principal Component Analysis • Machine Learning: The design and development of algorithms and techniques that allow computers to learn (complex, non-linear algorithms) e.g. Artificial Neural Networks, K-means clustering

27. Major Aspects of Chemoinformatics • Information Acquisition and Management: Methods for collecting data (mainly experimental). Development of databases for storage and retrieval of information. • Information Use: Data analysis, correlation and model building. • Information Application:Prediction of molecular properties relevant to chemical and biochemical sciences.

28. Prediction of Solubility, ADME & Toxicity Membrane transfer Liver extraction Dissolution Solid drug Systemic circulation Drug in solution Absorbed drug Solubility Absorption Metabolism

29. Prediction of biological activity/selectivity

30. Prediction models at CBS

31. Virtual screening • Computational techniques for a rapid assessment of large libraries of chemical structures in order to guide the selection of likely drug candidates. • Exploit knowledge of the active ligand molecule or theprotein target.

32. Virtual Screening Flavors TARGET-BASED 1D filters e.g. Lipinskis Rule of Five 1D LIGAND-BASED

33. Molecular similarity on the Chemical Space • Similar Property Principle – Molecules having similar structures and properties are expected to exhibit similar biological activity. (Not always true!) • Thus, molecules that are located closely together in the chemical space are often considered to be functionally related.

34. Ligand-based VS: Fingerprints • widely used similarity search tool • consists of descriptors encoded as bit strings • Bit strings of query and database are compared using similarity metric such as Tanimoto coefficient • MACCS fingerprints: 166 structural keys • that answer questions of the type: • Is there a ring of size 4? • Is at least one F, Br, Cl, or I present? • where the answer is either • TRUE (1) or FALSE (0)

35. Tanimoto Similarity or 90% similarity

36. Tanimoto Similarity

37. Ligand-based VS: Pharmacophore

38. Structure-based Virtual Screening: Docking Binding pocket of target Library of small compounds Given a protein and a database of ligands, docking scores determine which ligands are most likely to bind.

39. Energy of binding Binding pocket of target Library of small compounds -1 kcal/mol -10 kcal/mol +10 kcal/mol +1 kcal/mol ΔG= ΔH-TΔS Torsional free E vdW Hbond Desolvation E Electrostatic E

40. “Docking” and “Scoring” • Docking involves the prediction of the binding mode of individual molecules • Goal: new ligand orientation closest in geometry to the observed X-ray structure (Conformations of ligands in complexes often have very similar geometries to minimum-energy conformations of the isolated ligand) • Scoring ranks the ligands using some function related to the free energy of association of the two partners, looking at attractive and repulsive regions and taking into account steric and hydrogen bonding interactions • Goal: new ligand score closest in value to the docking score of the X-ray structure

41. Docking algorithms • Most exhaustive algorithms: • Accurate prediction of a binding pose • Most efficient algorithms • Docking of small ligand databases in reasonable time • Rapid algorithms • Virtual high-throughput screening of millions of compounds

42. Scoring functions • Molecular mechanics force field-based Score is estimated by summing the strength of intermolecular van der Waals and electrostatic interactions between all atoms of the ligand-target complex -CHARMM, AMBER • Empirical-based Based on summing various types of interactions between the two binding partners (hydrogen bonds, hydrophobic, …) - ChemScore, GlideScore, AutoDock • Knowledge-based Based on statistical observations of intermolecular close contacts from large 3D databases, which are used to derive potentials or mean forces -PMF, DrugScore

43. Combination of pharmacophore, docking and molecular dynamics (MD) screens Structure-based VS • better fit for analyzing smaller sets of compounds, especially in retrospective analysis • include all possible interactions thus allowing the detection of unexpected binding modes • Changing parameters for docking algorithms and scores is demanding Ligand-based VS • good enrichment of candidate molecules from the screening of large databases with less computational efforts • too coarse to pick up subtle differences induced by small structural variations in the ligands • many options for model refinement • Mutants are being developed: • pharmacophore methods with information about the target’s binding site • docking programs that incorporate pharmacophore constraints

44. http://www.vcclab.org/lab/edragon/

45. Public Web Chemoinformatics Toolshttp://pasilla.health.unm.edu/ http://pasilla.health.unm.edu/

46. ChemSpiderwww.chemspider.com

47. Open Babelhttp://openbabel.org/wiki/Main_page

48. D. Vidal et al, Ligand-based Approaches to In Silico Pharmacology, Chemoinformatics and Computational Chemical Biology, Ed J. Bajorath, Springer, 2011

49. Questions?