Chemical Data and Computer-Aided Drug Discovery

1. Chemical Data and Computer-Aided Drug Discovery Mike GilsonSchool of Pharmacy mgilson@ucsd.edu 2-0622

2. Outline Overview of drug discovery Structure-based computational methods When we know the structure of the targeted protein Ligand-based computational methods When we don’t know the protein’s structure

3. What is a drug?

4. Small Molecule Drugs Aspirin Sildenafil (Viagra) Taxol Darunavir Glipizide (Glucotrol) Digoxin

5. Nanoparticles(e.g., packaged small-molecule drugs) Doxil(liposome package, extended circulation time,milder toxicity) Abraxane(albumin-packaged taxol) http://www.doxil.com/about_doxil.html http://www.abraxane.com/professional/nab-technology.aspx

6. Biopharmaceuticals Etanercept (Enbrel)Protein with TNF receptor + AbFc domain Scavenges TNF, diminishes inflammation Erythropoietin (EPO)Stabilized variant of a natural protein hormone http://www.ganfyd.org/index.php?title=Erythropoietin_beta http://en.wikipedia.org/wiki/File:Enbrel.jpg

7. How are drugs discovered?

8. Natural Products Aspirin Digoxin Taxol Pacific Yew Foxglove Willow

9. How Aspirin Works Aspirin inflammation platelet activation platelet inactivation lipidlibrary.aocs.org/lipids/eicintro/index.htm

10. Biomolecular Pathways and Target SelectionE.g. signaling pathways Target protein http://www.isys.uni-stuttgart.de/forschung/sysbio/insulin/index.html

11. Empirical Path to Ligand Discovery Compound library(commercial, in-house,synthetic, natural) High throughput screening(HTS) Hit confirmation Lead compounds(e.g., µM Kd) Lead optimization (Medicinal chemistry) Animal and clinical evaluation Potent drug candidates(nM Kd)

12. Compound Libraries Government (NIH) Commercial (also in-house pharma) Academia

13. Computer-Aided Ligand Design Aims to reduce number of compounds synthesized and assayed Lower costs Less chemical waste Faster progress

14. 1. We Know the Structure of the Targeted ProteinStructure-Based Ligand Discovery HIV Protease/KNI-272 complex

15. Protein-Ligand Docking Structure-Based Ligand Design Potential functionEnergy as function of structure Docking softwareSearch for structure of lowest energy VDW - + Screened Coulombic Dihedral

16. Energy Determines Probability (Stability)Boltzmann distribution Energy Probability x

17. Structure-Based Virtual Screening 3D structure of target(crystallography, NMR, modeling) Compound database Virtual screening(e.g., computational docking) Candidate ligands Ligand optimizationMed chem, crystallography, modeling Experimental assay Ligands Drug candidates

18. Fragmental Structure-Based Screening 3D structure of target(crystallography, NMR, modeling) “Fragment” library Fragment docking Compound design Experimental assay and ligand optimizationMed chem, crystallography, modeling Drug candidates http://www.beilstein-institut.de/bozen2002/proceedings/Jhoti/jhoti.html

19. Potential Functions for Structure-Based Design Energy as a function of structure Physics-Based Knowledge-Based

20. Physics-Based PotentialsEnergy terms from physical theory Van der Waals interactions (shape fitting) Bonded interactions (shape and flexibility) Coulombic interactions (charge-charge complementarity) Hydrogen-bonding

21. Common Simplifications Used in Physics-Based Docking Quantum effects approximated classically Protein typically held rigid Configurational entropy neglected Influence of water treated crudely

22. Proteins and Ligand are Flexible Protein Ligand Complex DGo +

23. Binding Energy and Entropy EFree Unbound states EBound Bound states Entropy part Energy part

24. Structure-Based Discovery Physics-oriented approaches • Weaknesses • Fully physical detail becomes computationally intractable • Approximations are unavoidable • Parameterization still required • Strengths • Interpetable, provides guides to design • Broadly applicable, in principle at least • Clear pathways to improving accuracy • Status • Useful, far from perfect • Multiple groups working on fewer, better approxs • Force fields, quantum • Flexibility, entropy • Water effects • Moore’s law: hardware improving

25. Knowledge-Based Docking Potentials Histidine Ligandcarboxylate Aromaticstacking

26. Probability Energy Boltzmann: Inverse Boltzmann: Example: ligand carboxylate O to protein histidine N Find all protein-ligand structures in the PDB with a ligand carboxylateO For each structure, histogram the distances from O to every histidineN Sum the histograms over all structures to obtain p(rO-N) Compute E(rO-N) from p(rO-N)

27. Knowledge-Based Docking Potentials “PMF”, Muegge & Martin, J. Med. Chem. 42:791, 1999 A few types of atom pairs, out of several hundred total Atom-atom distance (Angstroms)

28. Structure-Based Discovery Knowledge-based potentials • Weaknesses • Accuracy limited by availability of data • Accuracy may also be limited by overall approach • Strengths • Relatively easy to implement • Computationally fast • Status • Useful, far from perfect • May be at point of diminishing returns

29. Limitations of Knowledge-Based Potentials 1. Statistical limitations (e.g., to pairwise potentials) 2. Even if we had infinite statistics, would the results be accurate? (Is inverse Boltzmann quite right? Where is entropy?) 100 bins for a histogram of O-N & O-C distances rO-C rO-N 10 bins for a histogram of O-N distances rO-N r2 r1 … r10

30. 2. We Lack the Structure of the Targeted ProteinLigand-Based Discovery e.g. MAP Kinase Inhibitors Using knowledge of existing inhibitors to discover more

31. Scenarios for Ligand-Based Discovery Experimental screening generated some ligands, but they don’t bind tightly A company wants to work around another company’s chemical patents An otherwise promising compound is toxic, is not well-absorbed, etc.

32. Ligand-Based Virtual Screening Compound Library Known Ligands Molecular similarity Machine-learning Etc. Candidate ligands OptimizationMed chem, crystallography, modeling Assay Actives Potent drug candidates

33. Sources of Data on Known LigandJournals, e.g., J. Med. Chem.

34. Some Binding and Chemical Activity Databases PubChem (NIH) pubchem.ncbi.nlm.nih.gov ChEMBL (EMBL) www.ebi.ac.uk/chembl BindingDB (UCSD) www.bindingdb.org

35. BindingDB www.bindingdb.org

36. Finding Protein-Ligand Data in BindingDB e.g., by Name of Protein “Target” e.g., by Ligand Draw  Search

37. Sample Query ResultsBindingDB to PDB

38. PDB to BindingDB

39. Sample Query Results Download data inmachine-readableformat

40. Machine-Readable Chemical Format Structure-Data File (SDF) PDB Format Lacks Chemical Bonding SDF Format Defines Chemical Bonds

41. There are Many Other Chemical File FormatsInterconvert with Babel

42. Chemical SimilarityLigand-Based Drug-Discovery Compounds(available/synthesizable) Similar Compare with known ligands Test experimentally Different Don’t bother

43. Chemical FingerprintsBinary Structure Keys carboxylate … aldehyde naphthyl S-S bond chlorine fluorine alcohol methyl ketone phenyl amide ethyl Molecule 1 Molecule 2

44. Chemical Similarity from FingerprintsTanimoto Similarity or Jaccard Index, T Molecule 1 Molecule 2 NI=2 Intersection NU=8 Union

45. Hashed Chemical Fingerprints Based upon paths in the chemical graph 1-atom paths: C F N H S O 2-atom paths: F-C C-C C-N C-S S-O C-H 3-atom paths: F-C-C C-C-N C-N-H C-S-O Each path sets a pseudo-random bit-pattern in a very long molecular fingerprint C S-O etc.

46. Maximum Common Substructure Ncommon=34

47. Potential Drawbacks of Plain Chemical Similarity May miss good ligands by being overly conservative Too much weight on irrelevant details

48. Scaffold Hopping Identification of synthetic statins by scaffold hopping Zhao, Drug Discovery Today 12:149, 2007

49. Abstraction and Identification of Relevant Compound Features Ligand shape Pharmacophore models Chemical descriptors Statistics and machine learning

50. Pharmacophore Models Φάρμακο (drug) + Φορά (carry) A 3-point pharmacophore Bulky hydrophobe 3.2 ±0.4 Å 5.0 ±0.3 Å + 1 Aromatic 2.8 ±0.3 Å