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Local properties on molecular surfaces

Explore the use of surface-based descriptors to describe and predict intermolecular interactions. Investigate the potential of local properties on molecular surfaces and their ability to replace traditional atom-atom approaches. Analyze a set of 26 descriptors and calculate their principal components to determine the dimensionality of physical property space.

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Local properties on molecular surfaces

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  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

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