Computer aided drug design(CADD)

Gives idea about computer aided drug design and software tools used for drug design

1. Computer aided drug design By Dr Sameh Ahmad M- Abdelghany

2. Basic Drug designing Section 01

3. Drug Design SLIDE 3 is the inventive process of finding new medications based on the knowledge of a biological target. It involves the design of molecules that are complementary in shape and charge to the biomolecular target with which they interact and therefore will bind to it.

4. Life Cylce of Drug Design SLIDE 4 Synthetic or Natural Compounds Preclinical Trails Clinical Trails 1 st step 2 nd step Drug 3 rd step Traditional Life Cycle

5. Modern Drug Design SLIDE 5 Target selection Identification of Target Verification of target Target Selection Lead Identification Screen Development High throughput screening Secondary assay Lead Optimization Lead explosion Potency in disease Pharmacokinetics Final 1 st step 2 nd step Drug 3 rd step

6. Drug Designing… SLIDE 6 Selected/designed molecule should be: Organic small molecule. Complementary in shape to the target. Oppositely charge to the biomolecular target .

7. Drug Designing… SLIDE 7 This molecule will: interact with target bind to the target activates or inhibits the function of a biomolecule such as a protein

8. Drug Designing… SLIDE 8 Drug design frequently but not necessarily relies on computer modeling techniques. This type of modeling is sometimes referred to as computer-aided drug design.

9. Mechanism based drug design SLIDE 9 When the disease process is understood at the molecular level and the target molecule(s) are defined, drugs can be designed specifically to interact with the target molecule in such a way as to disrupt the disease.

10. Computer-aided drug design(CADD) SLIDE 10 CADD represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions.

11. Introduction to CADD SLIDE 11 Drug design with the help of computers may be used at any of the following stages of drug discovery: hit identification using virtual screening (structure- or ligand-based design) hit-to-lead optimization of affinity and selectivity (structure-based design, QSAR, etc.) lead optimization: optimization of other pharmaceutical properties while maintaining affinity.

12. Objective of CADD SLIDE 12 To change from: Random screening against disease assays Natural products, synthetic chemicals To: Rational drug design and testing Speed-up screening process Efficient screening (focused, target directed) De novo design (target directed) Integration of testing into design process Fail drugs fast (remove hopeless ones as early as possible)

13. Types of drug design SLIDE 13 Ligand based drug design 2)Structure based drug design

14. Ligand-based drug design SLIDE 14 relies on knowledge of other molecules that bind to the biological target of interest. used to derive a pharmacophore model that defines the minimum necessary structural characteristics a molecule must possess in order to bind to the target.

15. Ligand-based drug design SLIDE 15 a model of the biological target may be built based on the knowledge of what binds to it, and this model in turn may be used to design new molecular entities that interact with the target. Alternatively, a quantitative structure-activity relationship (QSAR), in which a correlation between calculated properties of molecules and their experimentally determined biological activity, may be derived. These QSAR relationships in turn may be used to predict the activity of new analogs.

16. Structure-based drug design: SLIDE 16 relies on knowledge of the three dimensional structure of the biological target obtained through : x-ray crystallography NuclearMagnetic Resonance (NMR) spectroscopy. NMR spectroscopy X-ray crystallography

17. Structure-based drug design SLIDE 17 If an experimental structure of a target is not available, it may be possible to create a homology model of the target based on the experimental structure of a related protein. Homology modeling, also known as comparative modeling of protein, refers to constructing an atomic-resolution model of the "target" and an experimental three-dimensional structure of a related homologous protein (the "template").

18. Structure-based drug design SLIDE 18 Using the structure of the biological target, candidate drugs that are predicted to bind with high affinity and selectivity to the target may be designed using: interactive graphics Intelligence of a medicinal chemist. various automated computational procedures may be used to suggest new drug candidates.

19. Methods SLIDE 19 Virtual screening : The first method is identification of new ligands for a given receptor by searching large databases of 3D structures of small molecules to find those fitting the binding pocket of the receptor using fast approximate docking programs. de novo design of new ligands: In this method, ligand molecules are built up within the constraints of the binding pocket by assembling small pieces in a stepwise manner. These pieces can be either individual atoms or molecular fragments. The key advantage of such a method is that novel structures can be suggested. optimization of known ligands by evaluating proposed analogs within the binding cavity.

20. Binding site identification SLIDE 20 It is the first step in structure based design. relies on identification of concave surfaces on the protein that can accommodate drug sized molecules that also possess appropriate "hot spots" (hydrophobic surfaces, hydrogen bonding sites, etc.) that drive ligand binding.

21. Docking & Scoring SLIDE 21 Docking attempts to find the “best” matching between two molecules It includes finding the Right Key for the Lock To place a ligand (small molecule) into the binding site of a receptor in the manners appropriate for optimal interactions with a receptor. To evaluate the ligand-receptor interactions in a way that may discriminate the experimentally observed mode from others and estimate the binding affinity.

22. Components of Docking SLIDE 22 I- pre- and/or during docking : Representation of receptor binding site and ligand II- during docking : Sampling of configuration space of the ligand-receptor complex III- during docking and scoring : Evaluation of ligand-receptor interactions

23. Advantages of CADD SLIDE 23 Time Cost Accuracy information about the disease screening is reduced Database screening less manpower is required

24. Success stories of CADD SLIDE 24 K+ ion channel blocker structural based discovery Ca2+ antagonist / T-channel blocker chemical descriptor based discovery

25. Success stories of CADD SLIDE 25 Glyceraldehyde-phosphate DH inhibitors (anti- trypanosomatid drugs) combinatorial docking Thrombin inhibitor docking, de-novo design

26. Computational Tools For Drug Designing Section 02

27. Categories of software SLIDE 27 Databases & Draw Tools Molecular Modeling & Homology Modeling Binding site prediction & Docking Ligand design Screening -QSAR Binding free energy estimation ADME Toxicity

28. Databases SLIDE 28 ZincDatabase , Zinc15Database ChEMBL JChemforExcel ProteinDataBank (PDB) BindingMOAD ( MotherOfAllDatabase ) PDBbind STITCH,SMPDB

29. Databases SLIDE 29

30. Databases SLIDE 30

31. Draw Tools SLIDE 31 ChemDraw MarvinSketch ACD/ ChemSketch Marvin molecule editor and viewer ChemWriter UCSFChimera Pymol

32. Medchem SLIDE 32

33. SLIDE 33 UCSF Chimera

34. SLIDE 34 Chem Office

35. Molecular Modeling SLIDE 35 CHARMM GROMACS Amber SwissParam CHARMM-GUI CHARMMing.org SwissSideChain

36. Hyperchem SLIDE 36

37. Homology Modeling SLIDE 37 Modeller I-TASSER LOMETS SWISS-MODEL SWISS- MODELRepository Robetta

38. LOMETS SLIDE 38

39. Binding site prediction SLIDE 39 MED- SuMo CAVER FINDSITE sc -PDB Pocketome PocketAnnotatedatabase 3DLigandSite, metaPocket PocketAnnotate

40. CAVER SLIDE 40

41. Docking SLIDE 41 Autodock DOCK GOLD SwissDock DockingServer 1-ClickDocking iGemdock

42. iGemdock SLIDE 42

43. Screening SLIDE 43 Pharmer Catalyst PharmaGist SwissSimilarity Blaster AnchorQuery ligandscout Discovery Studio

44. Discovery Studio SLIDE 44

45. Target prediction SLIDE 45 MolScore -Antivirals MolScore -Antibiotics Swiss Target Prediction SEA ChemProt

46. Ligand design SLIDE 46 GANDI LUDI AutoT&T2 SwissBioisostere VAMMPIRE sc -PDB-Frag e-LEA3D eDesign iScreen

47. Binding free energy estimation SLIDE 47 Hyde, X-score NNScore DSXONLINE BAPPLserver BAPPL- Zserver ,

48. QSAR SLIDE 48 cQSAR clogP ClogP /CMR MOLEdb ChemDB /Datasets OCHEM E-Dragon PatternMatchCounter avogadro

49. Avogadro SLIDE 49

50. ADME Toxicity SLIDE 50 VolSurf GastroPlus MedChemStudio ALOGPS OSIRISPropertyExplorer SwissADME Metrabase PACT-F, TOXNET

51. GastroPlus SLIDE 51

52. That’s all. Thank you very much!  Any Questions?