développement "in silico" de nouveaux complexants de métaux alexandre varnek laboratoire...

Développement "IN SILICO" de nouveaux complexants

de métaux

Alexandre Varnek Laboratoire d’Infochimie,

Université Louis Pasteur, Strasbourg, FRANCE

COMPLEXATION

M1+

M2+

An - L

SOLVENT EXTRACTION

M1+

M2+

An-

L

- Acquisition of Data;

- Acquisition of Knowledge;

- Exploitation of Knowledge

« In silico » design of new complexants (extractants)

« In silico » design of new compounds

Generation of combinatorial

libraries

Models « structure-activity »

Database

Expert system

Clustering

Knowledge base

Screening

Hits

EXPERIMENT

I S I D AIn SIlico Design and data Analysis

Expert system

Generation of combinatorial

libraries

Database

Clustering

Knowledge base

Supplementary tools

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

•Databases development.

Acquisition of Data:

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).

• Development of an Expert System

Acquisition of Knowledge:

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, ….

QSAR / QSPRQSAR / QSPR

Hansch-type approach:Hansch-type approach: Property = Property = f f (physico-chemical, structural, … (physico-chemical, structural, … descriptors)descriptors)

CODESSA PRO program

Free-Wilson -type approach:Free-Wilson -type approach:

Property = Property = f f (fragment descriptors)(fragment descriptors)

TRAIL programSMF method

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

N

N

N

N

NHH

HH

HI. 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

N

N

N

N

NHH

HH

H

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

Fitting Equations

X= ao + ai Ni + , (1) i

X = ao + ai Ni + bi (2Ni 2

- 1) + , (2) i i

X = ao + ai Ni + bik Ni Nk + , (3) i i, k

SMF method

X : propertyai, bi : fitting coefficients Ni : number of the fragments of i-type : external descriptor (s)

TRAIL programSMF method

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.

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

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

• establishes reliable quantitative structure–property relationships

• must be very fast to analyse data sets of 104-106 compounds

Expert System

• Generation of virtual combinatorial libraries;• Screening and Hits selection.

Exploitation of Knowledge:

Generation of Virtuel Combinatorial Libraries

if R1, R2, R3 = and then

Markush structure P

O

R1 R3

R2

P

O

P

O

P

O

P

O

P

O

P

O

P

O

P

O

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.

COMPLEXATION

M1+

M2+

An - L

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 + acycl Ncycl (4) i

log = ao + ai Ni + bi (2Ni 2

- 1) + acycl Ncycl (5) i i

log = ao + ai Ni + bik Ni Nk + acycl Ncycl (6) i i, k

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)

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

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

Virtual Combinatorial Libraries of Podands

P

O

R1 R3

R2

R1, R2, R3 =

Me, Bu, Ph, Tol, CH2O(o-C6H4)P(O)Bu2, CH2O(o-C6H4)P(O)Ph2, CH2O(o-C6H4)P(O)Tol2,

CH2O(o-C6H4)CH2P(O)Bu2, CH2O(o-C6H4)CH2P(O)Ph2, CH2O(o-C6H4)CH2P(O)Tol2,

CH2O(o-C6H4)OCH2P(O)Bu2, CH2O(o-C6H4)OCH2P(O)Ph2, CH2O(o-C6H4)OCH2P(O)Tol2,

CH2CH2OCH2CH2(o-C6H4)P(O)Bu2, CH2CH2OCH2CH2(o-C6H4)P(O)Ph2,

CH2CH2OCH2CH2(o-C6H4)P(O)Tol2, o-C6H4OCH2P(O)Bu2, o-C6H4OCH2P(O)Ph2, o-

C6H4OCH2P(O)Tol2, CH2CH2OCH2P(O)Bu2, CH2CH2OCH2P(O)Ph2,

CH2CH2OCH2P(O)Tol2

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 compounds

« 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)

GDR PARIS

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 »

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

Molecular Molecular StructureStructure

Molecular Molecular StructureStructure ACTIVITIESACTIVITIESACTIVITIESACTIVITIES

RepresentationRepresentationRepresentationRepresentation Feature Selection & Feature Selection & MappingMapping

Feature Selection & Feature Selection & MappingMapping

DescriptorsDescriptorsDescriptorsDescriptors

Quantitative Structure Activity Relationships (QSAR)

(logD)exp (logD)calc

1.2 0.78

-0.20 -0.38

1.72 1.40

R = 0.973s = 0.071

-1 0 1 2-1

0

1

2

Extraction of UO22+ by phosphoryl-containing podands

calc

exp

n=24, R=0.956,F=235, s=0.18

Training stage

Validation stage

LogDcalc = 0.060 + 0.914 LogDexp

PO

OPO

OO

O

P P

O

O

OPOO

P O O P