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)

QSARQSAR

QQUANTITATIVEUANTITATIVE

SSTRUCTURETRUCTURE

AACTIVITYCTIVITY

RRELATIONSHIPSELATIONSHIPS

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

....un pò di STORIA....un pò di STORIA

(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

HanschHansch e Free Wilson comparisone Free Wilson comparison

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

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

Molecular Descriptors

selection

Y = f (x1…xn)

Genetic Algorithm

All Subset Models

Sequential Method

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

Casella Docente: Casella Docente: GramaticaGramaticaPaolaPaola

Casella Matricola Docente: Casella Matricola Docente: 000189000189

Casella Codice Corso: Casella Codice Corso: F76014F76014

Data di compilazione per esterso Data di compilazione per esterso 27 settembre 200527 settembre 2005