QSAR/QSPR: the Universal Approach to the Prediction of Properties of Chemical Compounds and Materials - PowerPoint PPT Presentation

1 / 40
About This Presentation
Title:

QSAR/QSPR: the Universal Approach to the Prediction of Properties of Chemical Compounds and Materials

Description:

... indices (Randic, c; Kier-Hall, mcv, solvation indices ... Electrotopological state ETS (Hall, Mohney, Kier) Steric. Bondi's van der Waals radius R ... – PowerPoint PPT presentation

Number of Views:433
Avg rating:3.0/5.0
Slides: 41
Provided by: Gen7156
Category:

less

Transcript and Presenter's Notes

Title: QSAR/QSPR: the Universal Approach to the Prediction of Properties of Chemical Compounds and Materials


1
QSAR/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
3
Fundamental Problem in Chemistry
Evaluation of relationships
between the structures of chemical
compounds and their properties or biological
activity
4
QSAR/QSPR General Approach
Model
F AF(S)
Predictivity
?A
Prediction
5
PROPERTIES
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.
6
Structural formula, Molecular graph,
Connectivity,
C2H6O
7
DESCRIPTORS 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)
8
Randic Index (c)
9
Prediction 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
10
The 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
11
Prediction 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
12
Sulfenamide 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
13
Prediction 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
14
Fragmental 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.

15
NEURAL NETWORK SOFTWARE NASAWIN
 
 
16
Fragmental descriptors in QSPR
17
Water Solubility
 
 
18
Boiling point 1 (diverse set of 885
compounds)
fragment types p1, p2, p3, p4, p5, p6, c3, c4,
c5, c6, s4, s5, s6
19
Boiling point (2)
20
Anticoccidial Activity of Triazinediones
 
 
21
Glass Transition Temperature of Polymers
22
Molar Heat Capacity of Polymers in the Liquid
State
23
Architecture 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)
24
EXAMPLES OF THE DIRECT STRUCTURE-PROPERTY
CORRELATIONS
 
  • Baskin, I. I. Palyulin, V. A. Zefirov, N. S.,
    J. Chem. Inf. Comput. Sci., 37, 715 (1997)

 
25
New approach in QSAR Neural Quantitative
Structure-Conditions-Property Relationships
R correlation coefficient St and Sv RMSE
for the training and validation sets
26
Molecular 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)
27
Molecular Supergraph Construction
28
Local 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

29
Affinity 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
30
Affinity 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
31
Affinity of substituted 2,5-diazabicyclo2.2.1he
ptanes to nicotinic acetylcholine receptor
Ki inhibition of competitive binding
R
Q
Lg
Ha
32
Affinity 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
33
Bradycardic 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-
34
Bradicardic 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
35
Bradicardic 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
36
Bradicardic 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
37
Bradicardic 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
38
Conclusions
  • 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).

39
AMPAreceptor modulators(ampakines)
40
The 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
41
Ligand-based drug design
3D-QSAR - approaches
Reconstruction of possible ligand binding site
on the basis of structures of known ligands
CoMFA/SYBYL
42
Model of (1) transport of ??2 ion through ion
channel of NMDA receptor, (2) blocking by Mg2 ,
(3,4) blocking by memantine
memantine
43
Ligand Binding with Ion Channel of NMDA-receptor
44
Quantitative Models of Ligand Binding with Ion
Channel of NMDA-receptor
Docking Alignment
Write a Comment
User Comments (0)
About PowerShow.com