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

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

3. « In silico » design of new compounds Combinatorial module Models « structure-activity » Database QSPR module Clustering module Knowledge base Screening Hits EXPERIMENT

4. I S I D A In SIlico Design and data Analysis QSPR module Database Clustering module Knowledge base Supplementary tools Combinatorial module

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

6. Expert System Acquisition of Knowledge: QSPR module Clustering module Knowledge base

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

8. 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 Substructural Molecular Fragments (SMF) method Fragment Descriptors: - atom/bond sequences from 2 to 6 atoms; - « augmented » atoms QSPR module

9. TRAIL program QSPR module

10. TRAIL procedure for the property X 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. QSPR module

11. 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

12. 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 Fragment descriptors from TRAIL could be used as « external » descriptors of CODESSA-PRO Supplementary QSPR tools

13. 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 %

14. 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

15. « Divide and Conquer » strategy for structurally diverse data sets The clustering module splits the initial data set into congeneric sub-sets for which QSPR models could be developed Clustering module

16. Knowledge Base The Knowledge Base: stores the QSPR models and predicts the properties ISIDA project

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

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

19. Applications - Complexation of crown-ethers with alkali cations - Extraction of UO 2 2+ by phosphoryl-containing podands ISIDA project

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

21. 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

22. Complexation of crown-ethers with alkali cations: macrocyclic effect log  = a o +  a i N i l og  = a o +  a i N i +  b i (2N i 2 - 1) log  = a o +  a i N i +  b ik N i N 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) acyclicmacrocyclic + a cycl N cycl

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

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

25. 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

26. 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

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

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

29. 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

30. 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

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

32. 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 Varnek, A.; Fourches, D.; Solov'ev, V.; Katritzky, A. R.; et al J. Chem. Inf. Comp. Sci. 2004, 44, 0000.

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

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