Title: QSAR/QSPR: the Universal Approach to the Prediction of Properties of Chemical Compounds and Materials
1QSAR/QSPR the Universal Approach to the
Prediction of Properties of Chemical Compounds
and Materials
V.A.Palyulin, I.I.Baskin, N.S.Zefirov
Department of Chemistry
Moscow State University
2 "Every attempt to employ mathematical methods in
the study of chemical questions must be
considered profoundly irrational and contrary to
the spirit of chemistry. If mathematical
analysis should ever hold a prominent place in
chemistry - an aberration which is happily almost
impossible - it would occasion a rapid and
widespread degeneration of that science." A.
Compte, 1798-1857
3Fundamental Problem in Chemistry
Evaluation of relationships
between the structures of chemical
compounds and their properties or biological
activity
4QSAR/QSPR General Approach
Model
F AF(S)
Predictivity
?A
Prediction
5PROPERTIES
Physico-chemical properties Boiling points,
melting points, density, viscosity, surface
tension, solubility in various solvents,
lipophilicity, magnetic susceptibility, retention
indices, dipole moments, enthalpy of formation,
etc. Biological activity IC50, EC50, LD50, MEC,
ILS, etc.
6Structural formula, Molecular graph,
Connectivity,
C2H6O
7DESCRIPTORS Topological indices Connectivity
indices (Randic, c Kier-Hall, mcv, solvation
indices mcs), Wiener W and expanded Wiener,
Balaban J, Gutman indices, Hosoya,
Merrifield-Simmons indices, indices based on
local invariants, informational indices,
Fragmental descriptors The number of fragments
of various size (chains, cycles, branched
fragments) in a molecule with several levels of
classification of atoms Physico-chemical
descriptors Indices based on atomic charges and
electronegativities, atomic inductive constants,
VdW volume and surface, H-bond descriptors,
Lipophilicity (Log P), Quantum-mechanical3D
Usp.Khim. (Russ.Chem.Rev.), 57 (3), 337-366 (1988)
8Randic Index (c)
9Prediction of Non-Specific Solvation Enthalpy of
Organic Compounds
Solvation enthalpy (kJ/mol)
Vaporization enthalpy (kJ/mol)
n 141 R 0.985 s 2.1
n 528 R 0.989 s 2.0
µ dipole moment 1?S 1-st order solvation
topological index Zi period number (measure of
atom size) di number of non-hydrogen neighbors
Dokl. Akad. Nauk, 1993, 331(2), 173-176
10The scheme of the design of new topological
indices (TIs)
a
Construction of graph matrices and their storage
Selection of functions
Selection of fragments
Construction of topological indices a) Using
matrices b) Using already constructed TIs
The set of constructed TIs for QSAR/QSPR studies
11Prediction of Diffusion of Small Molecules in
Polymers
n 14 R 0.989 s 0.103 F 145
Dokl. Akad. nauk. 1994 337 (2) 211-214
12Sulfenamide Vulcanization Accelerators
Resistance to preliminary vulcanization (min)
n 12 R 0.989 s 0.004 F 444
Vulcanization rate constant (min-1)
n 12 R 0.990 s 0.15 F 213
Maximum torque increase (Nm)
n 12 R 0.989 s 0.054 F 134
Dokl. Akad. nauk. 1993 333(2) 189-192
13Prediction of Mutagenicity of Substituted
Biphenyls
n 19 R 0.95 s 0.69 F 35
n 19 R 0.94 s 0.75 F 39.3
Nhis number of revertants Fr1-3 number of
fragments d1 minimum squared C-atom LUMO
contribution d2 minimum squared N-atom LUMO
contribution d3 maximum C-atom free valence
index d4 average O-atom free valence index
Fr1
Fr2
Fr3
Dokl. Akad. nauk. 1993 332(5) 587-589
14Fragmental Descriptors
- The numbers of fragments of various kind and
various size (chains, cycles, branched fragments)
in a molecule with several levels of
classification of atoms. For each molecule
hundreds of fragmental descriptors can be
computed. - If a structure-property data set is
sufficiently large to allow building
statistically significant models, then any
topological index can be replaced with a set of
substructural (or fragmental) descriptors.
15NEURAL NETWORK SOFTWARE NASAWIN
16Fragmental descriptors in QSPR
17Water Solubility
18Boiling point 1 (diverse set of 885
compounds)
fragment types p1, p2, p3, p4, p5, p6, c3, c4,
c5, c6, s4, s5, s6
19Boiling point (2)
20Anticoccidial Activity of Triazinediones
21Glass Transition Temperature of Polymers
22Molar Heat Capacity of Polymers in the Liquid
State
23Architecture of the Neural Device for Direct QSAR
- Neural device in application to the propane
molecule
Baskin, I. I. Palyulin, V. A. Zefirov, N. S.,
J. Chem. Inf. Comput. Sci., 37, 715 (1997)
24EXAMPLES OF THE DIRECT STRUCTURE-PROPERTY
CORRELATIONS
- Baskin, I. I. Palyulin, V. A. Zefirov, N. S.,
J. Chem. Inf. Comput. Sci., 37, 715 (1997)
25New approach in QSAR Neural Quantitative
Structure-Conditions-Property Relationships
R correlation coefficient St and Sv RMSE
for the training and validation sets
26Molecular Field Topology Analysis (MFTA)
Construction of Molecular Supergraph
Local descriptors - Electrostatic - Steric -
Lipophilic - Hydrogen bonding - Stereochemical -
Topological
Model building
Generation of novel promising structures
Palyulin, V. A. Radchenko, E. V. Zefirov, N.
S., J. Chem. Inf. Comput. Sci., 40, 659 (2000)
27Molecular Supergraph Construction
28Local Descriptors
- Sufficient coverage of major interaction types
- Easy extension of the descriptor set
- Electrostatic
- Gasteiger's atomic charge Q (electronegativity
equalization) - Absolute atomic charge Qa abs(Q)
- Sanderson's electronegativity ?
- Electrotopological state ETS (Hall, Mohney, Kier)
- Steric
- Bondi's van der Waals radius R
- Atomic contribution to the molecular van der
Waals surface S - Relative steric accessibility AS/Sfree
- Lipophilic
- Atomic lipophilicity contribution La
(environment-dependent - Ghose, Crippen) - Group lipophilicity Lg (atom and attached
hydrogens) - Hydrogen bonding
- Hydrogen bond donor (Hd) and acceptor (Ha)
ability of an atom (Abraham) - Stereochemical
- Local stereochemical indicator variables
29Affinity of substituted 2,5-diazabicyclo2.2.1he
ptanes to nicotinic acetylcholine receptor
Training set 31 compounds
R1 H, Me, CH2CN R2
R H, Me, F, Cl, Br, OH, NH2, OMe, CN, CH2NH2,
CONH2, NO2, PhCOO
30Affinity of substituted 2,5-diazabicyclo2.2.1he
ptanes to nicotinic acetylcholine receptor
Ki inhibition of competitive binding MED
minimum effective dose (hot plate test)
Predicted lg(1/Ki)
Experimental
31Affinity of substituted 2,5-diazabicyclo2.2.1he
ptanes to nicotinic acetylcholine receptor
Ki inhibition of competitive binding
R
Q
Lg
Ha
32Affinity of substituted 2,5-diazabicyclo2.2.1he
ptanes to nicotinic acetylcholine receptor
Construction of novel potentially active
structures
Total generated structures 171 5 best structures
wrt lg(1/Ki)
R1 Me, Et, CN, Pr, i-Pr, t-Bu, Ph,
4.01
3.69
??? R CH3, Cl, Br, NO2 R2 Me, Et, Pr, CN,
i-Pr, t-Bu
3.44
3.66
3.69
Activity range in training set -3.41 ... 2.05
33Bradycardic activity of 3,7,9,9-tetraalkyl-
3,7-diazabicyclo3.3.1nonanes
Training set 26 compounds
R1, R2 Me, Pr, i-Pr, Bu, i-Bu, C5H11, C6H13,
C10H21, CH2-c-Pr, CH2-c-C6H11, CHCH2,
CH2CH2CHCH2
R3, R4 Me, Et, Pr, Bu, -(CH2)3-, -(CH2)4-,
-(CH2)5-
34Bradicardic activity of 3,7,9,9-tetraalkyl-3,7-dia
zabicyclo3.3.1nonanes
SR75 ability to decrease pacemaker pulse
frequency (target effect) F75 ability to
decrease myocardium contraction force (side
effect) SelF selectivity wrt F FRP75 ability
to increase refractory period (side
effect) SelFRP selectivity wrt FRP
35Bradicardic activity of 3,7,9,9-tetraalkyl-3,7-dia
zabicyclo3.3.1nonanes
SR75 ability to decrease pacemaker pulse
frequency (target effect)
Predicted
Q
R
Experimental
36Bradicardic activity of 3,7,9,9-tetraalkyl-3,7-dia
zabicyclo3.3.1nonanes
SelF selectivity of antiarrhythmic activity wrt
myocardium contraction force
Predicted
Q
R
Experimental
Ha
37Bradicardic activity of 3,7,9,9-tetraalkyl-3,7-dia
zabicyclo3.3.1nonanes
Construction of novel potentially active
structures
Total generated structures 105 5 best structures
wrt SelF
R1, R3 Me, Et, Pr, i-Pr, t-Bu, R2 Me, Et,
Pr, i-Pr, t-Bu
70.75
70.74
63.83
63.82
63.12
Activity range in training set 0.4 ... 177
38Conclusions
- QSAR/QSPR (Quantitative structure-activity/proper
ty relationships) approaches can be considered as
universal techniques for the modeling and
prediction of nearly any properties of chemical
compounds and many properties of materials. - Some properties of materials can be
predicted as dependent on the structure of small
molecules used as additives (e.g. antioxidants,
etc.). - A number of properties of polymers had
been modelled as dependent of the chemical
structure of monomeric unit (e.g. glass
transition temperature, molar heat capacity for
liquid and solid state, dielectric constant,
refraction index).
39AMPAreceptor modulators(ampakines)
40The group of molecular design
Academician N. S. Zefirov Head of Organic
Chemistry Division
Dr. V.A. Palyulin Head of Group Dr. I.I.
Baskin Dr. A.A.Oliferenko Dr. E.V.Radchenko Dr.
M.I.Skvortsova Dr. I.G.Tikhonova Dr.
M.S.Belenikin Dr. A.A.Ivanov Dr.
A.Yu.Zotov S.A.Pisarev A.A.Ivanova A.A.Melnikov
41Ligand-based drug design
3D-QSAR - approaches
Reconstruction of possible ligand binding site
on the basis of structures of known ligands
CoMFA/SYBYL
42Model of (1) transport of ??2 ion through ion
channel of NMDA receptor, (2) blocking by Mg2 ,
(3,4) blocking by memantine
memantine
43Ligand Binding with Ion Channel of NMDA-receptor
44Quantitative Models of Ligand Binding with Ion
Channel of NMDA-receptor
Docking Alignment