1 / 46

Local properties on molecular surfaces

Local properties on molecular surfaces. Local properties on molecular surfaces. Tim Clark Computer-Chemie-Centrum Friedrich-Alexander-Universität Erlangen-Nürnberg. Descriptions of Molecules. Intermolecular Interactions. Physical components are well known Coulomb Donor/acceptor

fancy
Download Presentation

Local properties on molecular surfaces

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Local properties on molecular surfaces Local properties on molecular surfaces Tim Clark Computer-Chemie-Centrum Friedrich-Alexander-Universität Erlangen-Nürnberg

  2. Descriptions of Molecules

  3. Intermolecular Interactions • Physical components are well known • Coulomb • Donor/acceptor • Dispersion (and repulsion) • We are accustomed to atom-atom approaches • Force fields • QSAR and QSPR • Are there alternatives?

  4. QM-Based Descriptors • “Electronic“ • Molecular Electrostatic Potential (MEP) • Polarizability • Donor/Acceptor Properties • Multipole Moments • Molecular surface • Local properties at a surface • Isodensity (DFT, Murray and Politzer) • SES (fast) • Statistics of the local property as descriptors • MEP (Murray and Politzer)

  5. Surface Descriptors • MEP at the surface has a physical basis. • We should be able to describe intermolecular interactions using only surface properties. • Scaffold-Hopping is more likely if we use only surface-based descriptors. • Surface integral-models provide an interesting alternative to statistical QSPR • Atom-based simulation methods scale badly (because they treat atoms)...... BUT • Surface-based descriptors are expensive to calculate • ... and difficult to interpret.

  6. How Many Descriptors do we need for Physical Properties? (and what are they?) • Choose 26 descriptors that appear again and again in our QSPR-models • Calculate them for the entire Maybridge database • Calculate the principal components (factors) • What is the dimensionality of physical property space, what are the descriptors?

  7. PC-Eigenvalues: Scree Plot

  8. Prinvipal Components

  9. Physical property Space

  10. What is Missing? • Purely electrostatic interactions are described well • Donor/Acceptor, Electronegativity and Hardness are described by the atom-specific descriptors • Sums of potential-derived charges • Counts of H-bond donors and acceptors • Number of aromatic rings • ...... etc. • Can we design suitable local properties ?

  11. Local Ionization Energy Sjoberg, P.; Murray, J. S.; Brinck, T.; Politzer, P. A., Can. J. Chem. 1990, 68, 1440; Murray, J. S.; Abu-Awwad, F.; Politzer, P., THEOCHEM 2000, 501-502, 241; Hussein, W.; Walker, C. G.; Peralta-Inga, Z.; Murray, J. S., Int. J. Quant. Chem. 2001, 82, 160; Politzer, P.; Murray, J. S.; Concha, M. C., Int. J. Quant. Chem. 2002, 88,19.

  12. Local Ionization Energy IEL MEP

  13. Local Ionization Energy

  14. Other Local Properties • Local Electron affinity: • Local Hardness:

  15. Local Electron Affinity

  16. Local Electron Affinity Fukui Function

  17. Local Hardness

  18. Polarizabilty Variational method (Rinaldi and Rivail 1974) • Fast (no need for excited states) • Comparable to a population analysis

  19. Computer-Chemie-Centrum Universität Erlangen-Nürnberg Variational Method (AM1) Std. dev. = 2.99 Å3 PM3 : 4.44 Å3 MNDO : 1.94 Å3

  20. Computer-Chemie-Centrum Universität Erlangen-Nürnberg Parametrized Method (AM1)Test Set G. Schürer, P. Gedeck, M. Gottschalk, T. Clark, Int. J.Quant. Chem., 1999, 75, 17-31. Std. dev. = 0.70 Å3 PM3 : 0.74 Å3 MNDO : 0.78 Å3

  21. Atomic and “Orbital-“ Polarizabilities Partitioning: Additivity:

  22. One-Center Terms

  23. Two-Center Terms B. Martin, P. Gedeck, T. Clark, Int. J. Quant. Chem., 2000,77,473.

  24. The Additive Molecular Polarizability (AM1) Std. dev. = 0.59 PM3 : 0.65 MNDO : 0.60

  25. Atomic Polarizability Tensors: p-Bromotoluene

  26. Local Polarizability Density due to a singly occupied atomic orbital j Coulson population of atomic orbital j Mean polarizability calculated for atomic orbital j

  27. Local Polarizability

  28. MEP IEL EAL L L MEP 1 IEL 0.15 1 EAL -.12 0.18 1 L 0.21 0.81 -.44 1 L 0.29 0.19 0.51 -.14 1 Correlations Between Local Properties on Molecular Surfaces

  29. PC-Eigenvalues (Maybridge)

  30. Principal Components

  31. Boiling Points (N = 5453):Leave 10% out Cross-validation “old“ and “new“ descriptors 18 Descriptors (18:10:1 = 239 weights) MSE = 0.02 MUE = 17.3 RMSD = 24.9 10 Descriptors (10:9:1 = 128 weights) MSE = 0.3 MUE = 14.6 RMSD = 21.0

  32. Surface-integral models • P= target property • Ai = area of triangle i • ntri = number of triangles

  33. Surface-integral models • MolFESD: • Pixner, P.; Heiden, W.; Merx, H.; Möller, A.; Moeckel, G.; Brickmann, J. J. Chem. Inf. Comput. Sci.1994, 34, 1309-1319. • Jäger, T.; Schmidt, F.; Schilling, B.; Brickmann, J. J. Comput.-Aided Mol. Des.2000, 14, 631-646 • Jäger, R.; Kast, S. M.; Brickmann,. J. Chem. Inf. Comput. Sci.2003, 43, 237-247. • GB/PSA: • Best, S. A.; Merz, K. M., Jr.; Reynolds, C. H.. J. Phys. Chem. B1997, 101, 10479-10487.

  34. Free energies of hydration

  35. Free energies of hydration

  36. Free energies of solvation: n-octanol

  37. Free energies of solvation: chloroform

  38. Enthalpies of hydration

  39. Partial solvation Ligand Receptor Water

  40. Sources of data • The available data are limited in • Number • Quality • Use alternative sources • e.g. for solvation free energies • Gas phase proton affinites (G3) • pKas

  41. Physical-Property Mapping • Maybridge used as the “chemistry“ dataset • Use the top six principal components to train a 100  100 Kohonen net (unsupervised training) • 2,105 compounds selected from the World Drug Index as real drugs used as the drug dataset

  42. “Drugs“ “Drugs“ Physical Property Map Train Kohonen Net “chemistry“

  43. Physical Property Map: Drugs

  44. Physical Property Map: Hormones

  45. Model Applicabilty, Maps as Models? Aqueous solubility 550 (ompounds)

  46. Acknowledgments • Dr. Bernd Beck Dr. Andrew Chalk • Dr. Peter Gedeck Dr. Bill King • Dr. Harry Lanig Dr. Torsten Schindler • Dr. Cenk Selçuki Dr. Paul Winget • Matthias Brüstle Bernd Ehresmann • Matthias Hennemann Anselm Horn • Bodo Martin Gudrun Schürer • Kendall Byler Jr-Hung Lin • Dr. Tim F. Mitchell (Cambridge Combinatorial) • Prof. Johnny Gasteiger • Pfizer (Dr. Alexander Alex, Dr. Marcel de Groot) • Bayer Pharma (Dr. Andreas Göller, Dr. Jörg Kenderich) • 4SC Scientific (Dr. Thomas Herz) • Alexander-von-Humboldt Foundation • Hewlett-Packard

More Related