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Développement "IN SILICO" de nouveaux extractants et complexants
de métaux
Alexandre Varnek Laboratoire d’Infochimie,
Université Louis Pasteur, Strasbourg, FRANCE
- Acquisition of Data;
- Acquisition of Knowledge;
- Exploitation of Knowledge
« In silico » design of new complexants (extractants)
« In silico » design of new compounds
Combinatorial module
Models « structure-activity »
Database
QSPR module
Clustering module
Knowledge base
Screening
Hits
EXPERIMENT
I S I D AIn SIlico Design and data Analysis
QSPR module
Database
Clustering module
Knowledge base
Supplementary tools
Combinatorial module
•Expert System
Acquisition of Knowledge:
• establishes reliable quantitative structure–property relationships
• must be very fast to analyse data sets of 104-106 compounds
•Expert System
Acquisition of Knowledge:
QSPR module
Clustering module
Knowledge base
Quantitative Structure Activity Relationship (QSAR)
O
4
P O
O
N
Bu
Bu
PhX = f ( )
Quantitative Structure Property Relationships (QSPR)
X = distribution coefficient, extraction constant, ….
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
TRAIL programQSPR module
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
Complexation: Assessment of stability constants • phosphoryl-containing podands + K+ in THF/CHCl3
• crown-ethers + Na+, K+ and Cs+ in MeOH• -cyclodextrins + neutral guests in water
• Octanol / Water partition coefficients• Eight physical properties of C2 - C9 hydrocarbons
Test calculations
Solvent ExtractionExtraction constants UO2
2+ extracted in chloroform by phosphoryl-containing ligands
Distribution coefficients Hg, In or Pt extracted in DChE by phosphoryl-cont. podands UO2
2+ extracted in DChE by mono- and tripodands
UO22+ extracted in toluene by amides
Application of the SMF method
Biological propertiesAnti-HIV activity of HEPT, TIBO and CU derivatives
CODESSA PRO (Prof. A.R. Katritzky, Univ. of Florida, USA)
ConstitutionalTopologicalGeometrical ElectrostaticCharged Partial Surface Area (CPSA) Quantum-chemicalMO-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
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
Train
ing setT
est
Initial d
ata set
“Prediction” calculations using the best structure - property models
10 – 15 %
Property (X) predictions using best fit models
Compound model 1 model 2 … mean ± s
Compound 1 X11 X12 … <X1> ± X1
Compound 2 X21 X22 … <X2> ± X2
…
Compound m Xm1 Xm2 … <Xm> ± Xm
« 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
Knowledge Base
The Knowledge Base: stores the QSPR models and predicts the properties
ISIDA project
• Generation of virtual combinatorial libraries• Screening and Hits selection.
Exploitation of Knowledge:
R1 NR2
O
R3
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
Applications
- Complexation of crown-ethers with alkali cations
- Extraction of UO22+ by phosphoryl-containing podands
ISIDA project
Complexation of crown-ethers with alkali cations
OO
O
OO
O+ M+
OO
O
OO
OM+
Different properties compared to acyclic ligands: macrocyclic effect:
ME = (logK)crown - (logK)acyclic
OO
O
OO
O+ M+
OO
O
OO
OM+
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
OO
O
OO
O+ M+
OO
O
OO
OM+
Complexation of crown-ethers with alkali cations: macrocyclic effect
log = ao + ai Ni
log = ao + ai Ni + bi (2Ni 2
- 1)
log = ao + ai Ni + bik Ni Nk
L + M+ in MeOH:
acycl = 0.7
Na+ : Ncycl = 2 (15c5); 3 (18c6); 0 (other)
K+ : Ncycl = 2 (15c5); 5 (18c6); 3 (21c7); 2 (24c8 - 36c12); 0 (other)
acyclic macrocyclic
+ acycl Ncycl
+ acycl Ncycl
+ acycl Ncycl
OO
O O
OOO
OO
R3
R4
OOO
OO
OO
O
OO
O
OO
O
OO
O
OO
O
OO
OO
O
R1
R1
R1
R1
R2
R5
R5
R5
R5
n
R1 = H, Alk; n =1-7R2 = H, Alk, CH2(OH)CH2NHCH3
R3 = H, AlkR4 = CH2NH-Alk, CH2O-Alk, CH2(OCH2O)1,2-Alk
R5 = H, Alk, m = 1-3
m
Training stage
1 2 3 4 5 61
2
3
4
5
6
LogKcalc, mean
LogKexp
n=108, R2=0.952,F=2103, s=0.22
Crown-ethers with K+ in MeOH
Crown-ethers with K+ in MeOH
Validation stage
O
OO
OOOO
OO
O
OO
O
OO
O
OO
O
OO
OOO
O O
O
O
OO
OO
OO
O
OO
OO
O
OO
O
OO
O
OO
O
OO
O
O
O
O
O
O
O OO
OOO
OO
OO
OOO
1 2 3 4 5 6
2
3
4
5
LogKcalc, mean
LogKexp
n=11, R2=0.924,F=110.0, s=0.33
0.5 1.0 1.5 2.0 2.5 3.01.0
1.5
2.0
2.5
Acyclic polyethers with K+ in MeOH
“Prediction” of logKCH3
O OCH34 CH3
O OCH35 CH3
O OCH36
CH3
O OCH37 3
O OCH3
OHO O
CH3OH
4
OO
OCH3
OO
OCH3 OO
OCH3
OO
OCH3
3CH3 O
O O OCH3
3
CH3 OO O O
CH3
4 4
OO
O O
OHOH
OO
OO
O
OHOHOHOH
OO
O
LogKcalc, mean
LogKexp
n=13, R2=0.732,F=30.1, s=0.24
The ratio () of the average ME contribution and experimental logK for different macrocyclic scaffolds for Na+ (), K+ () and Cs+ () crown ether complexes respectively.
0.0
0.4
0.8
1.2
15c5 18c6 21c7 24c8 30c10
L + M+ in MeOH: estimation of the macrocyclic effect
= (acyclNcycl) / logK
SOLVENT EXTRACTION
M1+
M2+
An-
L
« In silico » design of new compounds
EXPERIMENT
Expert system
Generation of combinatorial
libraries
Models « structure-activity »
Screening
Database
Extraction of UO22+ by phosphoryl-containing podands:
QSPR modeling of distribution coefficient (logD)
PX
P
O O
R
R
R
R P
O
O X
Ph
PhPO
3
P
O
R
R
P X
O
OPh
Ph
Y YP X
O
R
R
PX
O
R
R
R = Ph, Tol, OEt
X = (CH2)n-O-(CH2)m, (CH2)n
Y = (CH2)n-O-(CH2)m, (CH2)n,
OCH2P(O)MeCH2O
calculations were performed for the initial data set of 32 podands as well as for two training (test) sets of 29 (3) compounds
Extraction of UO22+ 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
Generation of Virtual Extractants and Hits Selection
Generated Focussed Combinatorial Library of Podans:
2200 compounds
Hits selection
Screening
Blind test : are our predictions reliable ?!
-1
0
1
2
3
4
1 2 3 4 5 6 7 8
EXP
TRAIL
CODESSA - PRO
logD(UO22+)
N° of compound
Extraction properties for 7 of 8 new compounds have been correctly predicted
Synthesis
Extractionexperiments
P O
OPO
O
O
PO
OP
P O
O
O
O
P
P O
O
OP
POO
PO
O
PO
PO
O
O
POO
PO
O POP
O
OO
OO
P OPO
O
O PO
OP
O
1 2
3 4
5 6
7 8
Theoretically generated compoundsVarnek, A.; Fourches, D.; Solov'ev, V.; Katritzky, A. R.; et al J. Chem. Inf. Comp. Sci. 2004, 44, 0000.
« In silico » design of new compounds
EXPERIMENT
Expert system
Generation of combinatorial
libraries
Models « structure-activity »
Screening
Database
ACKNOWLEDGEMENTS
Denis FOURCHES Nicolas SIEFFERT
Dr Vitaly SOLOVIEV (IPAC, Russia)
Prof. Alan Katritzky (Univ. of Florida, USA)
Prof. G. Wipff
GDR PARIS
