“Drug Design” Introduzione alle metodologie ...dipbsf.uninsubria.it/qsar/education/Mat...
Transcript of “Drug Design” Introduzione alle metodologie ...dipbsf.uninsubria.it/qsar/education/Mat...
Dr. Ester PapaDr. Ester PapaDr. Ester Papa
QSAR and Environmental Chemistry Research UnitQSAR and Environmental Chemistry Research UnitQSAR and Environmental Chemistry Research Unit
DBSF - University of Insubria - Varese (Italy)DBSF DBSF -- University of University of InsubriaInsubria -- VareseVarese (Italy)(Italy)
“Drug Design”
Introduzione alle metodologie
QSAR
“Drug Design”“Drug Design”
IntroduzioneIntroduzione allealle metodologiemetodologie
QSARQSAR
http://http://dipbsf.uninsubria.it/qsardipbsf.uninsubria.it/qsar//
““Drug DesignDrug Design””
SiSi occupaoccupa delladella sintesi/progettazionesintesi/progettazione didi un un nuovonuovo farmacofarmaco
PossibilitàPossibilità
GuidareGuidare” la ” la sintesisintesi in in modomodo
razionale,“imparandorazionale,“imparando” ” daldal
giàgià notonoto, in termini , in termini didi
strutturastruttura molecolaremolecolare, ,
attivitàattività farmacologicafarmacologica e e
loroloro relazioni/dipendenzerelazioni/dipendenze..
SintetizzareSintetizzare un un nuovonuovo
farmacofarmaco per per piccolepiccole
modifichemodifiche strutturalistrutturali
((casualicasuali) ) didi unouno notonoto
NelNel Drug DesignDrug Design
sisi utilizzanoutilizzano tuttetutte le le informazioniinformazioni disponibilidisponibili
((proprietàproprietà chimicochimico--fisichefisiche, , farmacologichefarmacologiche, ,
effettieffetti collateralicollaterali, , eccecc) ) didi farmacifarmaci ((DrugDrug) ) giàgià notinoti, ,
per per progettareprogettare ((DesignDesign) e, se utile, poi ) e, se utile, poi
realizzarerealizzare la la sintesisintesi didi farmacifarmaci con con migliorimigliori
caratteristichecaratteristiche. .
ridurreridurre al al minimominimo la la sintesisintesi didi
farmacifarmaci “non “non utiliutili”, ”, sfruttandosfruttando le le
conoscenzeconoscenze giàgià a a disposizionedisposizione..
!!!SCOPO!!!SCOPO
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
E’ il campo di studio e ricerca chemoinformaticaE’ E’ ilil campo campo didi studio e studio e ricercaricerca chemoinformaticachemoinformatica
““ComputerComputer--Assisted Drug Design Assisted Drug Design (CADD)(CADD)””
Attività Attività farmacologicafarmacologica
++
RelazioniRelazioni QSARQSAR
progettazioneprogettazione e la e la sintesisintesi didi prodottiprodotti con con
attivitàattività farmacologichefarmacologiche ottimaliottimali..
TrimethoprimTrimethoprim(antibiotico)(antibiotico)
Studi QSAR
basati sull’applicazione di diversi metodi
matematici e statistici
(metodi chemiometrici)
con lo scopo di trovare relazioni
quantitative (modelli matematici)
Y= b0 + b1X1 + b2X2 +…….bnXn
tra struttura molecolare (descrittori
molecolari: Xn) e attività biologica o
farmacologica (Y)
ComputerComputer--Assisted Molecular Design Assisted Molecular Design (CAMD) o Modelling (CAMM)(CAMD) o Modelling (CAMM)
�� RappresentazioneRappresentazione didi proprietàproprietà strutturalistrutturali delladella
molecolamolecola mediantemediante descrittoridescrittori molecolarimolecolari
MedianteMediante l’utilizzol’utilizzo didi adattiadatti computer (work stations) e computer (work stations) e
adeguatiadeguati calcolicalcoli quantisticiquantistici o o didi meccanicameccanica molecolaremolecolare sisi
ottieneottiene unauna rappresentazionerappresentazione spazialespaziale (3D) (3D) delladella
molecolamolecola organicaorganica ((ilil farmacofarmaco) ) nellenelle sue sue possibilipossibili
conformazioniconformazioni e e delladella proteinaproteina ((ilil recettorerecettore))
�� Studio Studio didi interazioniinterazioni molecolamolecola organicaorganica--recettorerecettore
proteicoproteico (“docking”)(“docking”)
�� ConfrontiConfronti tratra molecolemolecole a a strutturastruttura diversadiversa
((allineamentoallineamento))
PuntiPunti salientisalienti del Drug Designdel Drug Design
�� Studio del Studio del recettorerecettore : : strutturastruttura 3D 3D delladella proteinaproteina
�� Studi di Modellistica Molecolare perStudi di Modellistica Molecolare perstrutture 3D e analisi strutture 3D e analisi conformazionaleconformazionaledi farmaci (di farmaci (ligandoligando) )
�� Interazione farmacoInterazione farmaco--recettore: recettore: dockingdocking
�� Sviluppo di modelli matematici QSAR:Sviluppo di modelli matematici QSAR:es. es. CoMFACoMFA (Comparative (Comparative Molecular Field Molecular Field Analysis) Analysis)
☺☺ SintesiSintesi piùpiù miratamirata
☺☺ RiduzioneRiduzione didi costicosti
☺☺ RisparmioRisparmio didi tempo tempo
�� VantaggiVantaggi
è è NECESSARIONECESSARIO avereavere espertiesperti
didi QSAR in Drug DesignQSAR in Drug Design
PROPRIETA’ CHIMICOPROPRIETA’ CHIMICO--FISICHEFISICHE
COMPOSTI CHIMICICOMPOSTI CHIMICI
PRODOTTI NATURALIPRODOTTI NATURALI XENOBIOTICIXENOBIOTICI
SINTESI
Degradazione
Persistenza
Bioaccumulo
Ripartizione
DESTINO E COMPORTAMENTO DESTINO E COMPORTAMENTO AMBIENTALEAMBIENTALE
Tossicità
Mutagenicità e Carcinogenicità
AttivitàAttività FarmacologicaFarmacologica
ATTIVITA’ BIOLOGICAATTIVITA’ BIOLOGICA
THE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSE
QSAR
22.000.000 in C.A.S.
100.000 on market
EINECS TSCA
5%
known
data
Environmental fate?Environmental fate?Environmental fate?
Human effects?Human effects?Human effects?
NEW1.500.000 / year
NEW2.000 / year
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
experiments
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese(Italy)
APPLICATIONS of QSAR PREDICTIONSAPPLICATIONS of QSAR PREDICTIONSAPPLICATIONS of QSAR PREDICTIONS
Filling of data gaps
Validation of experimental data
Screening, ranking and priority setting
Highlighting chemicals of interest (also before their synthesis)
PRIORITY LISTSPRIORITY LISTSPRIORITY LISTS
In DRUG DESIGN modelling and prediction of In DRUG DESIGN modelling and prediction of
pharmacological activity : to direct the synthesis of new pharmacological activity : to direct the synthesis of new
drugsdrugs
Minimize animal testingMinimize animal testingOptimize industry resourceOptimize industry resourceallocationallocation
(Q)SAR History(Q)SAR History(Q)SAR History
AlkaneAlkane m.p. and b.p.m.p. and b.p.((CrosCros, 1863), 1863)
Alcohol water Alcohol water solubilitysolubility
n.Cn.CM.WM.W..
n.Cn.CM.WM.W..
PHYSICOPHYSICO--CHEMICAL CHEMICAL PROPERTIESPROPERTIES
STRUCTURESTRUCTURE
PHYSICOPHYSICO--CHEMICAL CHEMICAL PROPERTIESPROPERTIES
BIOLOGICAL BIOLOGICAL ACTIVITYACTIVITY
BIOLOGICAL BIOLOGICAL ACTIVITYACTIVITY
STRUCTURE/STRUCTURE/PROPERTIESPROPERTIES
((HanschHansch 1964)1964)
Alcohol toxicityAlcohol toxicity part. part. coeffcoeff..fat/waterfat/water
(Meyer(Meyer--Overton 1899Overton 1899--1901)1901)
Log PLog P
Classical QSAR Classical QSAR analysis (analysis (HanschHansch and and Free Wilson)Free Wilson)
consider only 1Dconsider only 1D--2D 2D structures. structures.
Their main Their main characteristic is the characteristic is the substitution variation of substitution variation of a common scaffold.a common scaffold.
3D3D--QSAR analysis (QSAR analysis (eses. . CoMFACoMFA) has a much ) has a much broader scope. broader scope.
It starts from 3DIt starts from 3D--structuresstructures
Correlates biological Correlates biological activities with 3D activities with 3D property fields.property fields.
Different QSAR ApproachesDifferent QSAR Approaches
Hybrid Approach Hybrid Approach 1D1D--2D and 3D structures2D and 3D structures
Different kind of descriptorsDifferent kind of descriptors
The Hansch Approach (1964)The The HanschHansch Approach Approach (1964)(1964)
“The structure of a chemical influences its “The structure of a chemical influences its
properties and biological activity”properties and biological activity”
“Similar compounds behave similarly”“Similar compounds behave similarly”
Activity or Property = f (Structure)Activity or Property = f (Structure)
PREDICTED DATAPREDICTED DATAPREDICTED DATA
Relationship (f)
between Structure and
chemical’s behavior
(Activity or Property)
Drug Transport Drug Transport
and Drug Receptor Interactionand Drug Receptor Interaction
The “random walk” process:The “random walk” process:
drugdrug
receptorreceptor
(binding site)(binding site)
aqueous phases and aqueous phases and lipophiliclipophilic barriersbarriers
Biological activity= f (Biological activity= f (transport+bindingtransport+binding))
Biological activity = kBiological activity = k11 + k+ k22 ((lipolipo)+ k)+ k33 (elett)+k(elett)+k44 ((sterster))
Classical Hansch equation:Classical Classical HanschHansch equation:equation:
“Biological Activity” = a + b logP + c E + d S
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese(Italy)
logPor log Kow, partition coefficient
between octanol and water: hydrophobicity term
Eelectronic
term
Ssteric term
related to bulk and shape
The possibility of the chemical to interact with the target and to be
active
The probability or ability of the chemical to reach the
target site
Congenericity principleCongenericity principle: : substituentsubstituent variation variation of a common basic of a common basic structurestructure
Multiple Linear Regression QSAR modelMultiple Linear Regression QSAR model
equation and parametersequation and parameters
Molecular Properties Molecular Properties and and Hansch’sHansch’s parametersparameters
Hammett’s EquationHammett’s Equation
Example1: Example1: Approach to Approach to phenethylaminesphenethylaminesby by HanschHansch and Free Wilsonand Free Wilson
From Classic to 3DFrom Classic to 3D--QSARQSARSpecifics of Drug ActionSpecifics of Drug Action
LipophilicityLipophilicity and dissociation/ionizationand dissociation/ionization are are
responsible for responsible for transport and dissociationtransport and dissociation of drugsof drugs
in biological systems.in biological systems.
The The geometric fitgeometric fit and the and the complementaritycomplementarity of the of the
surface 3D properties of a surface 3D properties of a ligandligand are responsible for its are responsible for its
affinity affinity to a binding siteto a binding site..
Which is the biologically active Which is the biologically active conformationconformation??
Conformation Conformation in in vacuovacuo
Conformation in the crystalConformation in the crystal
Conformation in aqueous solutionConformation in aqueous solution
Conformation at the binding siteConformation at the binding site
3D3D--QSAR: QSAR: CoMFACoMFA
CampiCampi molecolarimolecolari stericisterici ed ed elettrostaticielettrostatici
....ma in pratica.......ma in pratica...
COME SI FA???COME SI FA???
L’ABC del “buon”QSARL’ABC del “buon”QSAR
CHEMICALSCHEMICALSCHEMICALS
MOLECULAR DESCRIPTORSMOLECULAR MOLECULAR
DESCRIPTORSDESCRIPTORS
R1, R2, R3: mathematical relationships
R1R1 R2R2
R3R3PHYSICO-CHEMICAL
PROPERTIES
PHYSICOPHYSICO--CHEMICALCHEMICAL
PROPERTIESPROPERTIES
ExperimentalExperimental
datadata
BIOLOGICAL
ACTIVITIES
BIOLOGICAL BIOLOGICAL
ACTIVITIESACTIVITIES
ExperimentalExperimental
datadata
THE 3 NECESSITIES:THE 3 NECESSITIES:THE 3 NECESSITIES:
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
MEANINGFUL STRUCTURAL INFORMATIONMEANINGFUL STRUCTURAL INFORMATION
Good representation of the chemical structure:
molecular descriptorsmolecular descriptors
GOOD INPUT DATAGOOD INPUT DATA
High-quality experimental dataexperimental data as input data to find
the Structure - Activity Relationship
PREDICTIVE MODELSPREDICTIVE MODELS
Quantitative modelsQuantitative models with validated predictivepredictive
performances (chemometric methods)
Experimental data setExperimental data setExperimental data set
The models will only be as good as the data used The models will only be as good as the data used to develop them!to develop them!
There is a need for a “limited” number of There is a need for a “limited” number of HIGHHIGH--QUALITYQUALITY
experimental dataexperimental data on which to develop QSAR models!on which to develop QSAR models!
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
CORRECTCORRECT
REPRESENTATIVEREPRESENTATIVE
HOMOGENEOUSHOMOGENEOUS
NEEDS FOR EXPERIMENTAL DATA:NEEDS FOR EXPERIMENTAL DATA:
AS NUMEROUS AS POSSIBLEAS NUMEROUS AS POSSIBLE
MOLECULAR
DESCRIPTORS
MOLECULAR MOLECULAR
DESCRIPTORSDESCRIPTORS
HowHow cancan we use we use thethe chemical structurechemical structure??????
They transform They transform
the structure the structure
in numbers!!!in numbers!!!CAS AMW Sv Ss Mv Me Ms
000050-29-3 12.66 21.69 45.81 0.77 1.03 2.41
000050-30-6 12.73 11.22 33.89 0.75 1.06 3.08
000050-31-7 15.03 11.92 37.67 0.79 1.09 3.14
000050-32-8 7.89 23.59 37.33 0.74 0.98 1.87
000051-28-5 10.83 11.14 49 0.66 1.1 3.77
000051-44-5 12.73 11.22 33.89 0.75 1.06 3.08
000055-38-9 8.98 19.38 35.81 0.63 1.01 2.24
000055-63-0 10.77 11.67 60.83 0.56 1.14 4.06
000056-23-5 30.76 5 17.69 1 1.21 3.54
000056-38-2 9.1 19.71 49.31 0.62 1.03 2.74
000057-15-8 11.83 9.6 24.83 0.64 1.05 3.1
000057-74-9 17.07 19.79 46.81 0.82 1.07 2.6
000058-89-9 16.16 13.79 32.67 0.77 1.07 2.72
000058-90-2 17.84 11.11 32.78 0.85 1.1 2.98
000059-50-7 8.91 10.6 23.11 0.66 1.01 2.57
000060-29-7 4.94 7.5 10.5 0.5 0.98 2.1
000060-51-5 9.55 14.17 31.97 0.59 1.02 2.66
Those numbersThose numbers areare used as variables to used as variables to developdevelop QSARQSAR modelsmodels
….. MOLECULAR DESCRIPTORS….. MOLECULAR DESCRIPTORS….. MOLECULAR DESCRIPTORS
.. ..·· ··
······ ··
·· ······
·· ··..
..
..
......
.. ..CC
CC
CC
CC
CC CC
CC CC
CCCC
CCCC
C lC l C lC l
C lC l C lC l
HH
HH
HH
HH
HH
HH
.. ..·· ··
······ ··
·· ······
·· ··..
..
..
......
.. ..
1D1D1D
3D3D3D
2D2D2D
ClClClCl
ClCl ClCl
CCll
ClCl
CCll
CCll
HH
HH
HH
HH
HH
HH
0D0D0D
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese(Italy)
The “magic” molecular descriptorThe “magic” molecular descriptorThe “magic” molecular descriptor
molecularfragments
C log PSoftware
OH
Cl
Bioconcentration
Sorption
Water solubility
Toxicity
Cell membrane penetration:
Activity in the cells
Log P (or Log P (or KowKow))
QuantumQuantum--Chemical Descriptors (EChemical Descriptors (EHOMOHOMO EELUMOLUMO), atomic charges, ), atomic charges,
polarizabilitypolarizability… (from … (from semiempiricalsemiempirical calculations)calculations)
PhysicoPhysico--chemical Properties (i.e. chemical Properties (i.e. LogPLogP, Solubility etc…) , Solubility etc…)
Utility of the Structural InformationUtility of the Structural Information
To verify the structural similarity or dissimilarityTo verify the structural similarity or dissimilarity
To model by regression or non linear methods a quantitative To model by regression or non linear methods a quantitative response (numerical value of a property or an activity)response (numerical value of a property or an activity)
To model by classification methods a qualitative response To model by classification methods a qualitative response (active/not active)(active/not active)
To select the representative chemicals for the splitting into To select the representative chemicals for the splitting into training/test setstraining/test sets
...some other descriptors...some other descriptors
EXPERIMENTAL EXPERIMENTAL DATADATA
XX
11
..
..
..
.n.n
XX
11
..
..
..
.n.n
xx
nn
....
..YY
Input Data
matrix
Input DataInput Data
matrixmatrix
MOLECULAR MOLECULAR DESCRIPTORSDESCRIPTORS
Quantitative modelsQuantitative models
forfor
quantitative responsesquantitative responses
Quantitative modelsQuantitative models
forfor
qualitative responsesqualitative responses
Chemometric Chemometric MethodsMethods
REGRESSION METHODSREGRESSION METHODS
-- Multivariate Linear Regression (MLR)
-Partial Least Squares Regression (PLS)
CLASSIFICATION METHODSCLASSIFICATION METHODS-- Classification Tree (CART)- Discriminant Analysis- Neural Networks
EXPLORATIVE ANALYSISEXPLORATIVE ANALYSIS-- Principal Component Analysis
- Cluster Analysis
Chemometric Chemometric MethodsMethods
CHEMICALSCHEMICALS
Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)
SPLITTINGSPLITTING
Experimental DesignExperimental Design--Similarity AnalysisSimilarity Analysis
--DD--optimal Designoptimal Design
--Factorial DesignFactorial Design
...about predictive models......about predictive models...
DATA SETDATA SET
TRAINING SETTRAINING SETTRAINING SET TEST SETTEST SETTEST SET
PredictivityPredictivity
NEW DATANEW DATA
INTERNAL VALIDATION
Q2LOO
Q2LMO
EXTERNAL VALIDATION
Q2EXT
FITTINGR2
REGRESSION REGRESSION MODELMODEL
Prof. Paola Gramatica Prof. Paola Gramatica -- QSAR Research Unit QSAR Research Unit -- DBSF DBSF -- University of University of InsubriaInsubria -- VareseVarese(Italy)(Italy)
LIMITATIONS OF QSAR MODELSLIMITATIONS OF QSAR MODELSLIMITATIONS OF QSAR MODELS
�� Statistical qualityStatistical qualityFitting RFitting R22
PredictivityPredictivity QQ22
�� OutliersOutliers
�� Chemical domainChemical domain
Exp. responseExp. response
PredPred. response. response
�� Prediction reliabilityPrediction reliability
REVERSIBLE REVERSIBLE
DECODINGDECODING
CHEMICALSCHEMICALS
MODELMODEL
YY XX
FITTINGFITTING MAXIMUMMAXIMUM
PREDICTIVE POWERPREDICTIVE POWER
EXPERIMENTALEXPERIMENTALDATADATA
MOLECULARMOLECULARDESCRIPTORSDESCRIPTORS
NEWNEWCHEMICALSCHEMICALS
MOLECULARMOLECULARDESCRIPTORSDESCRIPTORS
??????PREDICTIONPREDICTION
Sviluppo di Modelli QSAR per la Sviluppo di Modelli QSAR per la
predizione dell’attività predizione dell’attività citotossicacitotossica nella nella
terapia terapia fotodinamicafotodinamica di 34 di 34 ArylAryl PorfirinePorfirine
Lab. Lab. Prof.Prof. BanfiBanfi
Sintesi Sintesi delle molecoledelle molecole
Lab. Lab. Prof.Prof. MontiMonti
Test in Test in vitrovitro
Lab. Lab. Prof.Prof. GramaticaGramatica
Sviluppo di modelli Sviluppo di modelli e e
predizione dell’attività predizione dell’attività per 5 nuovi compostiper 5 nuovi composti
AR1
NH
NNH
N
AR2AR1
NH
NNH
N
AR2
AR1
NH
NNH
N
AR3
AR4
monoAryl-porphyrin diAryl-porphyrin tetraAryl-porphyrin
DRAGON data
39 5 261 10 0
No. MolID Num.ArticoloNomeHIN Name AMW Mv Me Ms ARR RBF nDB nAB
1 1 3 1 AG1 7.83 0.69 0.99 2.02 0.742 0.071 2 46
2 2 14 2 AG2 7.9 0.68 1 2.06 0.833 0.066 0 40
3 3 7 3 AG3 8.18 0.71 1 2.17 0.881 0.045 0 52
4 4 20 4 AG4 8.37 0.7 1.01 2.26 0.889 0.03 0 40
5 5 8 5 LB1 7.98 0.71 0.99 1.97 0.952 0.031 0 40
6 6 11 6 LB2 7.84 0.66 1.01 2.13 0.741 0.091 0 40
7 7 10 7 LB3 7.92 0.69 1 2.03 0.87 0.056 0 40
8 8 9 8 LB4 7.92 0.69 1 2.03 0.87 0.056 0 40
9 9 22 9 LB5 8.05 0.71 0.99 1.99 0.971 0.019 0 34
10 10 37 10 LB6 8.01 0.7 1 2.03 0.919 0.035 0 34
11 11 39 11 LB7 8.21 0.71 1 2.11 0.944 0.019 0 34
12 12 23 12 LB8 8.01 0.7 1 2.03 0.919 0.035 0 34
13 13 38 13 LB9 8.21 0.71 1 2.11 0.944 0.019 0 34
14 14 21 14 LB10 7.74 0.7 0.99 1.94 0.881 0.03 0 37
15 15 36 15 LM1 7.88 0.71 0.99 1.95 0.929 0.047 0 52
16 16 26 16 LM2 7.82 0.68 1 2.04 0.719 0.078 2 46
17 17 25 17 LM3 7.82 0.68 1 2.04 0.719 0.078 2 46
-4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5
Y-Exp.
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
Y-P
red.
Training
Test
Unknown
27
32
36
39
38
37
Log 1/ICLog 1/IC5050= = --10.17( 10.17( ±±±±±±±±3.85) +21.09(3.85) +21.09(±±±±±±±±2.29)GATS6v 2.29)GATS6v -- 40.57(40.57(±±±±±±±±8.19)PW3 +38.9(8.19)PW3 +38.9(±±±±±±±±13.21)R4u+13.21)R4u+
nntrainingtraining=23 =23 nnvalidationvalidation=11 =11 nnunknownunknown=5 R=5 R22=0.86 Q=0.86 Q22=0.81 Q=0.81 Q22BootBoot=0.80 Q=0.80 Q22
extext=0.78=0.78
Molecola NON ATTIVAMolecola NON ATTIVASintesi Sconsigliata!!Sintesi Sconsigliata!!
Modello di regressione QSARModello di regressione QSARAttività Attività citotossicacitotossica delle delle porfirineporfirine
Molecole ATTIVEMolecole ATTIVESintesi Consigliata!!Sintesi Consigliata!!
Corso di Drug design 2005/6
Martedì e Giovedì ore 14-17
N.B. Lezioni da 3 ore!!
Lez. Data Docente Argomento
1 27.9.05 Papa (Gramatica) Introduzione
2 dal 4.10.05 Pollegioni La struttura delle proteine
3 Pollegioni Classificazione strutturale delle proteine
4 Pollegioni Classificazione strutturale delle proteine; le
proteine virali
5 Pollegioni Determinazione delle strutture 3D delle
proteine; dinamica ed evoluzione delle
strutture proteiche
6 Pollegioni Interazione (macro)molecolare
7 Pollegioni –
esercitazione al
computer
Esercitazione al computer: visualizzazione e
modificazione di strutture 3D di proteine
8
27.10.05 Pollegioni Metodi razionali e combinatoriali di
modificazione di strutture proteiche
3-5.11.05 Verifica di Biochimica
9 8.11.05 Maiocchi Visualizzazione e simulazione delle proprietà
stereo-elettroniche delle molecole attraverso la
grafica e la modellistica molecolare
10 Maiocchi Progettazione di nuovi farmaci in assenza di
informazioni strutturali sul recettore: metodi
per la costruzione e validazione di un
farmacoforo. Utilizzo del farmacoforo nella
esplorazione di database di strutture chimiche
11 Bonati Applicazioni del docking molecolare nel
Drug Design
12 Bonati Basi chimico fisiche dell'interazione legante-
proteina. Metodologie computazionali per la
modellistica del binding
13 Bonati Tecniche di docking molecolare
14 24.11.05 Maiocchi Sviluppo di nuovi farmaci: ottimizzazione
delle proprietà di assorbimento, distribuzione,
metabolismo ed escrezione