Introduction to Ligand- Based Drug Design Chimica ......Rino Ragno: Computational Medicinal...
Transcript of Introduction to Ligand- Based Drug Design Chimica ......Rino Ragno: Computational Medicinal...
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Rino Ragno: Computational Medicinal Chemistry Applications to Epigenetic Targets Inhibitors 409-09-2015
Conformational Search Molecular Docking Simulate Annealing
Molecular Dynamics Graphical Visualization 3-D QSAR Pharmacophore
Ab Initio QM
QSAR, COMBINE, Scoring Functions, Homology Modeling,…..
Rino Ragno: Computational Medicinal Chemistry Applications to Epigenetic Targets Inhibitors 509-09-2015
Basic Research
Target Ident
Target Valid
Hit Ident
Hit to Lead
Lead Opt Preclin Clinical
Trial Prod Diagnos
Drug Design = Computational & Synthesis Tandem
Rino Ragno: Computational Medicinal Chemistry Applications to Epigenetic Targets Inhibitors 609-09-2015
Protein Structure
Unknown Known
Liga
nd S
truc
ture U
nkno
wn
Library Screening De Novo
Know
n
Ligand Based Structure-Based
Ligand-Based
QSAR Phamacophore 3-D QSAR
Structure-Based
ScoringFunctionDocking
COMBINE
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The Hansch Equation
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The first QSAR equations were based on the observation that partition coefficients, as expressed by log P values, are to some
extent, correlated to certain biological endpoints.
log (1/C) = k1 log P + k2σ + k3
Conc. of compound requiredto produce a standard response
in a given t
Logarithm of the molecule’s partition coefficient
(1-octanol/water)
Hammet Parameter(molecule’s electronic
characteristics)
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Identification of Active Ligands
Identification of Suitable Descriptors (molecular fingerprint)
Establish Mathematical Expression Relating Descriptors to Activity
Construction and Validation of the QSAR model
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Squared CorrelationCoefficient R2 or r2
Cross-Validated R2 Q2 or q2
∑
∑
=
=
−
−−= N
ii
N
iicalci
YY
YYr
1
2exp,
1
2,exp,
2
)(
)(1
∑
∑
=
=
−
−−= N
ii
N
iipredi
YY
YYq
1
2exp,
1
2,exp,
2
)(
)(1
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Squared CorrelationCoefficient R2 or r2
∑
∑
=
=
−
−−= N
ii
N
iicalci
YY
YYr
1
2exp,
1
2,exp,
2
)(
)(1
10 2 ≤≤ r
∑
∑
=
=
−
−−≡−≡= N
ii
N
iicalci
YY
YY
TSSRSS
TSSESSr
1
2exp,
1
2,exp,
2
)(
)(11
Fitting
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Cross-validated R2 Q2 or q2
12 ≤≤∞− q∑
∑
=
=
−
−−= N
ii
N
iipredi
YY
YYq
1
2exp,
1
2,exp,
2
)(
)(1
The predictive ability of a model is estimatedusing a reduced set of structural data
CV (Cross-Validation)
N
YYSDEP
N
iipredi∑
=
−= 1
2,exp, )(
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Y-Scrambling
Scrambling
Scrambled Y vector
A new model is obtained for such permuted data, R2 and Q2 are then recalculated.This step is repeated for a sufficient number of times (iterations):
a good number being 50 to 100.
Values obtained in the above fashion are compared with the true values obtained for the modelthat was fitted on the real data.
Original X block
A statistical test of prediction tools, in which models are fitted for randomly reorderedproperty/activity values and compared with the model obtained for the actualproperty/activity values.
A new model is obtained for suchpermuted data, R2 and Q2 are thenrecalculated.This step is repeated for a sufficientnumber of times (iterations):
a good number being 50 to 100.
Values obtained in the above fashion arecompared with the true values obtained forthe model that was fitted on the real data.
Original Y vector
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N
YYSDEP
N
iipredi∑
=
−= 1
2,exp, )(
N° of predicted compounds
External Test-SetSDEP (Standard Deviation Error of Prediction)