1. Développement "IN SILICO" de nouveaux complexants de métaux Alexandre Varnek Laboratoire d’Infochimie, Université Louis Pasteur, Strasbourg, FRANCE

2. COMPLEXATION M1+M1+ M2+M2+ An - L

3. SOLVENT EXTRACTION M1+M1+ M2+M2+ An - L

4. - Acquisition of Data; - Acquisition of Knowledge; - Exploitation of Knowledge « In silico » design of new complexants (extractants)

5. « In silico » design of new compounds Generation of combinatorial libraries Models « structure-activity » Database Expert system Clustering Knowledge base Screening Hits EXPERIMENT

6. I S I D A In SIlico Design and data Analysis Expert system Generation of combinatorial libraries Database Clustering Knowledge base Supplementary tools

7. Informational System for Complexation (Extraction) Expert system Generation of combinatorial libraries Database Comprehensive Solvent eXtraction Database Substructural Molecular Fragments method Generation of focussed libraries using molecular fragments Design of new compounds

8. Databases development. Acquisition of Data:

9. Comprehensive Solvent Extraction Database (SXD) Two of six informational pages of SXD “in house” One record per extraction equilibrium (90 fields). It contains bibliography + system description + 2D and 3D structures of extractants + thermodynamic and kinetic data (in textual, numerical and graphical forms).

10. Development of an Expert System Acquisition of Knowledge:

11. Quantitative Structure Activity Relationship (QSAR) X = f ( ) Quantitative Structure Property Relationships (QSPR) X = distribution coefficient, extraction constant, ….

12. QSAR / QSPR Hansch-type approach: Property = f (physico-chemical, structural, … descriptors) Hansch-type approach: Property = f (physico-chemical, structural, … descriptors) CODESSA PRO program Free-Wilson -type approach: Property = f (fragment descriptors) TRAIL programSMF method

13. The SMF method is based on the representation of a molecule by its fragments and on the calculation of their contributions to a given property. V. P. Solov’ev, A. Varnek, G. Wipff, J. Chem. Inf. Comput. Sci., 2000, 40, 847-858 A. Varnek, G. Wipff, V. P. Solov’ev, Solvent Extract. Ion. Exch., 2001, 19, 791-837 A. Varnek, G. Wipff, V. P. Solov’ev, J. Chem. Inf. Comput. Sci., 2002, 42, 812-829 V. P. Solov’ev, A. Varnek, J. Chem. Inf. Comput. Sci., 2003, 43, 1703 - 1719 Fragment Descriptors: Substructural Molecular Fragments (SMF) method

14. I. Sequences II. Augmented Atoms Substructural Molecular Fragments method Type of Fragments C-N=C-H C-N=C N=C-N C-N N=C C-H I(AB, 2-4) sequence Atoms+Bonds 2 to 4 atoms

15. I. Sequences II. Augmented Atoms Type of Fragments II(Hy) (hybridization of neighbours is taken into account) II(A) (no hybridization) Substructural Molecular Fragments method

16. Fitting Equations X= a o +  a i N i + , (1) i X = a o +  a i N i +  b i (2N i 2 - 1) + , (2) i i X = a o +  a i N i +  b ik N i N k + , (3) i i, k SMF method X : property a i, b i : fitting coefficients N i : number of the fragments of i-type  : external descriptor (s)

17. TRAIL program SMF method

18. TRAIL procedure for the property X SMF method 1. Training stage generates 147 computational models involving 49 types of fragments and 3 fitting equations; uses all generated models in order to fit fragment contributions; applies statistical criteria to select the “best fit” models for the Training set; 2. Prediction stage applies the best models to “predict” properties of compounds from the Test and/or CombiLibrary sets.

19. Complexation: Assessment of stability constants phosphoryl-containing podands + K + in THF/CHCl 3 crown-ethers + Na +, K + and Cs + in MeOH  -cyclodextrins + neutral guests in water Octanol / Water partition coefficients Eight physical properties of C 2 - C 9 hydrocarbons Test calculations Solvent Extraction Extraction constants UO 2 2+ extracted in chloroform by phosphoryl-containing ligands Distribution coefficients Hg, In or Pt extracted in DChE by phosphoryl-cont. podands UO 2 2+ extracted in DChE by mono- and tripodands UO 2 2+ extracted in toluene by amides Application of the SMF method Biological properties Anti-HIV activity of HEPT, TIBO and CU derivatives

20. CODESSA PRO (Prof. A.R. Katritzky, Univ. of Florida, USA) Constitutional Topological Geometrical Electrostatic Charged Partial Surface Area (CPSA) Quantum-chemical MO-related Thermodynamical The program uses about 700 Physico-Chemical Descriptors

21. Fitting and validation of structure – property models Building of structure - property models Selection of the best models according to statistical criteria Splitting of an initial data set into training and test sets Training set Test Initial data set “Prediction” calculations using the best structure - property models 10 – 15 %

22. Property (X) predictions using best fit models Compoundmodel 1model 2…mean ± s Compound 1X 11 X 12 … ±  X 1 Compound 2X 21 X 22 … ±  X 2 … Compound mX m1 X m2 … ±  X m

23. establishes reliable quantitative structure–property relationships must be very fast to analyse data sets of 10 4 -10 6 compounds Expert System

24. Generation of virtual combinatorial libraries; Screening and Hits selection. Exploitation of Knowledge:

25. Generation of Virtuel Combinatorial Libraries if R1, R2, R3 = andthen Markush structure

26. Program CombiLib generates virtual combinatorial libraries based on the Markush structures when selected substituents are attached to a given molecular core.

27. COMPLEXATION M1+M1+ M2+M2+ An - L

28. Complexation of crown-ethers with alkali cations Different properties compared to acyclic ligands: macrocyclic effect: ME = (logK) crown - (logK) acyclic

29. Complexation of crown-ethers with alkali cations - Estimation of stability constants for acyclic analogues of crowns - Estimation of macrocyclic effect - QSPR modelling on structurally diverse data set Goal: A. Varnek, G. Wipff, V. P. Solov’ev, J. Chem. Inf. Comput. Sci., 2002, 42, 812-829

30. Complexation of crown-ethers with alkali cations: macrocyclic effect log  = a o +  a i N i + a cycl N cycl (4) i l og  = a o +  a i N i +  b i (2N i 2 - 1) + a cycl N cycl (5) i i log  = a o +  a i N i +  b ik N i N k + a cycl N cycl (6) i i, k L + M + in MeOH: a cycl = 0.7 Na + : N cycl = 2 (15c5); 3 (18c6); 0 (other) K + : N cycl = 2 (15c5); 5 (18c6); 3 (21c7); 2 (24c8 - 36c12); 0 (other)

31. Training stage LogK calc, mean LogK exp n=108, R 2 =0.952, F=2103, s=0.22 Crown-ethers with K + in MeOH

32. Validation stage LogK calc, mean LogK exp n=11, R 2 =0.924, F=110.0, s=0.33

33. Acyclic polyethers with K + in MeOH “Prediction” of logK LogK calc, mean LogK exp n=13, R 2 =0.732, F=30.1, s=0.24

34. The ratio (  ) of the average ME contribution and experimental logK for different macrocyclic scaffolds for Na + (), K + () and Cs + () crown ether complexes respectively. 15c5 18c6 21c7 24c8 30c10 L + M + in MeOH: estimation of the macrocyclic effect  = (a cycl N cycl ) / logK

35. SOLVENT EXTRACTION M1+M1+ M2+M2+ An - L

36. Extraction of UO 2 2+ by phosphoryl-containing podands: QSPR modeling of distribution coefficient (logD) R = Ph, Tol, OEt X = (CH 2 ) n -O-(CH 2 ) m, (CH 2 ) n Y = (CH 2 ) n -O-(CH 2 ) m, (CH 2 ) n, OCH 2 P(O)MeCH 2 O calculations were performed for the initial data set of 32 podands as well as for two training (test) sets of 29 (3) compounds

37. Extraction of UO 2 2+ by podands: QSPR modeling of logD Fragment descriptors, TRAIL: 3 models Pre-selected 262 « classical » descriptors, CODESSA: 0 models (!) Mixed (16 fragment + 262 « classical ») descriptors, CODESSA: 2 models

38. Virtual Combinatorial Libraries of Podands R1, R2, R3 = Me, Bu, Ph, Tol, CH 2 O(o-C 6 H 4 )P(O)Bu 2, CH 2 O(o-C 6 H 4 )P(O)Ph 2, CH 2 O(o- C 6 H 4 )P(O)Tol 2, CH 2 O(o-C 6 H 4 )CH 2 P(O)Bu 2, CH 2 O(o-C 6 H 4 )CH 2 P(O)Ph 2, CH 2 O(o- C 6 H 4 )CH 2 P(O)Tol 2, CH 2 O(o-C 6 H 4 )OCH 2 P(O)Bu 2, CH 2 O(o-C 6 H 4 )OCH 2 P(O)Ph 2, CH 2 O(o-C 6 H 4 )OCH 2 P(O)Tol 2, CH 2 CH 2 OCH 2 CH 2 (o-C 6 H 4 )P(O)Bu 2, CH 2 CH 2 OCH 2 CH 2 (o- C 6 H 4 )P(O)Ph 2, CH 2 CH 2 OCH 2 CH 2 (o-C 6 H 4 )P(O)Tol 2, o-C 6 H 4 OCH 2 P(O)Bu 2, o- C 6 H 4 OCH 2 P(O)Ph 2, o-C 6 H 4 OCH 2 P(O)Tol 2, CH 2 CH 2 OCH 2 P(O)Bu 2, CH 2 CH 2 OCH 2 P(O)Ph 2, CH 2 CH 2 OCH 2 P(O)Tol 2

39. Generation of Virtual Extractants and Hits Selection Generated Focussed Combinatorial Library of Podans:  2200 compounds Hits selection Screening

40. Blind test : are our predictions reliable ?! logD(UO 2 2+ ) N° of compound Extraction properties for 7 of 8 new compounds have been correctly predicted Synthesis Extraction experiments 12 34 56 78 Theoretically generated compounds

41. « In silico » design of new compounds EXPERIMENT Expert system Generation of combinatorial libraries Models « structure-activity » Screening Database

42. ACKNOWLEDGEMENTS Denis FOURCHES Nicolas SIEFFERT Dr Vitaly SOLOVIEV (IPAC, Russia) Prof. Alan Katritzky (Univ. of Florida, USA) n GDR PARIS

44. Joseph Louis Gay-Lussac, Mémoires de la Société d ’Arcueil 2:207 (1808) « We are perhaps not far removed from the time when we shall be able to submit the bulk of chemical phenomena to calculation »

45. Tools for searching and records preparation Structure-Data-File Editor (2D structures + properties) MOL Editor (2D structures) · Internal Text Editor ·Digitazer (converts a graph represented as image into data table Y=F(X)) · Searching Options (textual and numerical fields) (Sub) Structural Search ( internal 2D editor + searching engine) Solvent eXtraction Database (SXD) Labo d’Infochimie

46. Molecular Structure ACTIVITIESACTIVITIES RepresentationRepresentation Feature Selection & Mapping DescriptorsDescriptors Quantitative Structure Activity Relationships (QSAR)

47. (logD) exp (logD) calc 1.20.78 -0.20-0.38 1.721.40 R = 0.973 s = 0.071 Extraction of UO 2 2+ by phosphoryl-containing podands calc exp n=24, R=0.956, F=235, s=0.18 Training stage Validation stage LogD calc = 0.060 + 0.914 LogD exp