Lecture1 Pharmacophores & MolecularSimilarity · 2012-01-18 · Lecture1 Pharmacophores &...

Lecture 1

Pharmacophores & Molecular Similarity

Invited Guest ProfessorshipInvited Guest Professorship

Université Lois Pasteur, Strasbourg

Prof. Dr. Gisbert Schneider

Goethe-University, Frankfurt

18 November 2008, (c) G. Schneider

“[..] Thus between A & B immense gap of relation. C & B the finest

Transmutation of species

gap of relation. C & B the finest gradation, B & D rather greater distinction. Thus genera would be formed. — bearing relation”

Charles R. Darwin (*1809)Notebook B: Transmutation of Species (1837-1838), p.36

Source: www.darwin-online.org.uk

HO O

O

OH

8 7 9

1

2

3

4

5

6 10

13

11

12

Representations of Aspirin®

4

Discrete Approximation: Atom

• Van der Waals radius“effective size”

(in Ångstöm; 1 Å = 10-10 m)

• Atom = “hard sphere”

Element r / ÅH 1.2

à Corey-Pauling-Koltun (CPK) model

OH

rH 1.2F 1.35O 1.4N 1.55C 1.7S 1.85Cl 1.8P 1.9

ClF

O OH

O

O

5

Discrete Approximation: Surface

ProbeSphereH2O: r = 1.4 Å

vdW surface

Solvent accessible surface (SAS)Connolly surface

H2O: r = 1.4 Å

Lee-Richardssurface

6

N

N

N

H2N

O

HN

O

OH

Molecular Interaction:

Shape Complementarity

Receptor Ligand

N N

NH2

2OH

O OH

Dihydrofolatereductase (DHFR)

Protein Data Bank (PDB) entry: 3dfr

Methotrexate

www.pdb.org

7

Concept of Local Atom Environments

Atom properties are determined by their local environment.

à topological environment (molecular graph)

à spatial (3D) environment (3D model, conformation)à spatial (3D) environment (3D model, conformation)

Carbon atomswith different properties

Oxygen atomswith different properties

8

O

O

O OH

Aspirin®

What is a Molecule?

Aspirin®

Increasing “Fuzziness”

• Pharmacophoric representationon different levels of abstraction

à scaffold-hopping

9

Pharmacophore Definition

GLOSSARY OF TERMS USED IN MEDICINAL CHEMISTRY

(IUPAC Recommendations 1998) http://www.chem.qmul.ac.uk/iupac/medchem/

Pharmacophore (pharmacophoric pattern)

A pharmacophore is the ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target structure and to trigger (or to block) its biological response.biological target structure and to trigger (or to block) its biological response.

(A pharmacophore does not represent a real molecule or a real association of functional groups, but a purely abstract concept that accounts for the common molecular interaction capacities of a group of compounds towards their target structure.)

Pharmacophoric descriptors

Pharmacophoric descriptors are used to define a pharmacophore, including H-bonding, hydrophobic and electrostatic interaction sites, defined by atoms, ring centers and virtual points.

10

N

OH

Receptor

• H-bond donors• H-bond acceptors• Lipophilic• Positive• Negative

• H-bond donors• H-bond acceptors• Lipophilic• Positive• Negative

Potential Pharmacophore Points (PPP)

Pharmacophoric Features

OH

Ligand • Negative• Negative

• e.g., bitstring representation: 00010100010110011

11

HN

NH

O NH2

N

O

OH HN NH

O

Donor

Acceptor

Donor + Acceptor

Pharmacophoric Types of Functional Groups

N

OH

ON

N

HN

NH

S

O

O

CF3

NH2

NNH

NH2

O

ON

O

ON

Donor + Acceptor

Acid(negative ionizable)

Base(positive ionizable)

Atoms excluded(non pharmacophoric)

12

sp3

NH sp3

OH

H

NH sp3

H

NH sp2,aromatic

H

Nsp2,aromatic

HN

sp2

sp3

NH sp3

H

NH sp3

H,C

NC,H sp2,aromatic

HN

any O, except NO2

Hydrogen bonddonors

Hydrogen bondacceptors

Extended List of Pharmacophoric Points

Positively chargedor ionizable

Negatively chargedor ionizable

Lipophilic

N+

sp3

Nsp3sp3

sp3

NH sp3

H

NH sp3

HO

C,S,PO

CC,F,Cl,Br,I,-S- H

S S S Cl,Br,I

13

Scaffold-hopping:

From function to molecular structure

à Exploit multiple binding behavior of targets QSAR Comb. Sci. (2003) 22:713

O

NH

O

S

NH

O

NO2

Cl

GW9662

PPARγ

NN

S

O

O

N

O

NHS

Pioglitazone

COOH

HN

N

N

14

Re-purposing:

From molecular structure to function

à Exploit multiple binding behavior of ligands J. Med. Chem. (2002) 45:137

OH

HO O

OOH

Phosphodiesterase-III

CF conductance regulator

α-glucosidase

L-type Ca2+ channel

GABAa

Topoisomerase II

Estrogen receptor betaGenisteinGenistein

HN

N

S

N

N

Olanzapine(Zyprexa®)

H1 receptorDopamine D1Dopamine D3Dopamine 4.2α-1 adrenoceptorα-2 adrenoceptorSerotonin 5-HT2ASerotonin 5-HT2CMuscarinergic receptor

15

How to Find More Chemotypes

1) Ask a medicinal chemist

2) “Fuzzify” the compound sampling procedure

• Fuzzy Pharmacophores• Shape-based Virtual Screening / Sampling• Shape-based Virtual Screening / Sampling

3) Use natural product-derived building blocks

4) Employ “de novo” design

Renner & Schneider (2004) J. Med. Chem. 47:4653.Schneider et al. (2006) QSAR Comb. Sci. 22:713

16

Applications: Scaffold-hopping

Calcium T-channel blocker Angew. Chem. Int. Ed. (1999) 38:2894

N

N

HN

OO

O

F

F

F

N

N

NHO

Cl

Mibefradil

CATS

Kv1.5 potassium channel blocker Angew. Chem. Int. Ed. (2000) 39:4130

S

O

O

NH

HO

HN

O

O

S

O

O

NH HN

O

CATS

17

CATSCATS

Chemically Advanced Template SearchChemically Advanced Template Search

18

S

O

ONH

N

O

P

Histogramx = {0.2, 0.1, 0.3, ...}

Structure

a)

CATS: Topological Pharmacophores

L

LL

LL

L LL

L

A

AD

L

L

L A,P

L

L

L

A

L

A

1

2

3

4

56

7

8

9

Bin

• Count all atom pairs (15 x 10)• Scale by number of non-H atoms

1 … 150b)

c)

d)

19

Shortest Paths: Flood-fill

S

O

ONH

N

O

Molecular graph

A

Molecular graph

A

B

dAB = 8 bonds

20

0.5

1

1.5

2

2.5

3

3.5

4

Counts

0.5

1

1.5

2

2.5

3

3.5

4

Counts

“Fuzzification” of the CATS2D Descriptor

1bin+⋅= CountsfCounts

1bin−⋅= CountsfCounts

0.5

0 1 2 3 4 5

Distance / bonds

0.5

0 1 2 3 4 5

Distance / bonds

OriginalOriginal FuzzyFuzzy

• no significant overall improvement of enrichment of activesin a focused library

• can be helpful for individual searches (à scaffold hopping)21

N Cl

OHO

F

N Br

OHO

F

N

N

O

HN

OH

OO

N

N

OHO

1

2

6

7

CATS2D: A Ranked List

Query

HaloperidolD2-antagonist

D2 ligand

D2 ligand

5-HT2C antagonist

H3 antagonist

Ki (D1) = 270 nMKi (D3) = 21nMKi (D4.2) = 11nMKi (5-HT2A) = 25nMKi (α1) = 19 nMKi (H1) = 730 nM.

Ki (D1) = 270 nMKi (D3) = 21nMKi (D4.2) = 11nMKi (5-HT2A) = 25nMKi (α1) = 19 nMKi (H1) = 730 nM.

NNO

HO

HNS

O

O

O

N

NHO

N

SNH2

NN

N

OF

N

Cl

OHF

N

FF

FF

NN

H2N

2

3

4

5

7

8

9

10

D2 ligand

D2 ligand

D2 ligand

H3 antagonist

TNF-α inhibitor

Eliprodil(ion channel)

GABA transportertype I (ion channel)

PPAR-γ agonist

22

CATS2DCATS3D

COX-2

CATS2DCATS3D

CATS2DCATS3D

HIV-ProteaseMMP

Similarity Searching → Complementary Results

Charge3D Charge3D Charge3D

∩∩∩∩ = 6∩∩∩∩ = 6 ∩∩∩∩ = 1∩∩∩∩ = 1 ∩∩∩∩ = 0∩∩∩∩ = 0

23

COX-2: buried, narrow HIV-Prot: buried “tunnel”MMP3: shallow, solvent-exposed

Ligand Flexibility & Receptor Shape

24

Red: crystal structure of L-735,524Green: CORINA model

1CX2 1D5J 1HSG

Identification of Natural Product-Derived

5-Lipoxygenase Inhibitors

OR1

O N

R3

R2

H

O

O

Oα-santonin

H

Mugwort (Artemisia vulgaris)

EC50 ≈ 0.8 µM (5-LOX)

O O

HN N

S

25

3-Point Pharmacophores3-Point Pharmacophores

26

Topological 3-Point Pharmacophores (3PP)

NH2

O

OH

2D structure Molecular graph

Greene et al. (1994) JCICS 34:1297.Good & Kuntz (1995) JCAMD 9:373.

McGregor & Muskal (1999) JCICS 39:569.

d = 3

PPP assignment

d = 5

d = 4

Distance assignment(in bonds)

x = 10010 ...

Molecular fingerprint(bitstring)

27

Calculation of Feature Relevance

))0(())1(( =−== iii FfFfR xx

x : molecular fingerprint with features F

Bit set Bit not set

2 23

3(2+3=5)

Rj = 2

Ri = 3

PPP assignment

Byvatov et al. (2005) ChemBioChem 6:997.Franke et al. (2005) J. Med. Chem. 48:6997.

à Visualization

à Feature Weighting

28

3-Point Pharmacophore Screening:

COX-2 Ligands (1)

N S NH2

O

planar

hydrophobic

H-bond

Ring B

Sulfonyl groupa) b)

NNF3C

S NH2

O

H-bondacceptor

Ring A

SC-558 CelecoxibPalomer et al. (2003)

29

OHO

O

OH

SO

OHOO

N

NS

O

O

NH2

O

N

Cl

S

O

NH

N

O

S

O

O

OH

NS

O

O

S OS

O

O

NH2

Cl

O

O

H2N

1 2 3 4

SHN

NO

O O

Suggested compounds

IC50 = 0.2 ± 0.3 µMIC50 = 0.2 ± 0.3 µM

IC50 = 8 ± 2 µMIC50 = 8 ± 2 µM


COX-2 Ligands (2)

NS

O

NH2 OO

NO O

S N ON

N

O

O

NN

N

SH

O

S

O

O

NH2

N

SS N O

O

O

N

N S

O

O

NH2

OO

S

O

O

O

NNF3C

S NH2

O

ONH

Cl

Cl

O

OH

5 6 7 8

9 10 11 12

13 14 15 16

NO

S

N

N

SO

O

NH2

N

Celecoxib RofecoxibDiclofenac

Positive control

IC50 = 15 ± 3 µMIC50 = 15 ± 3 µMIC50 = 6 ± 3 µMIC50 = 6 ± 3 µMIC50 = 5 ± 1 nMIC50 = 5 ± 1 nM

30

Automated Docking(1cx2, GOLD)

SC-558

Molecule 5

His90

?


COX-2 Ligands (3)

à new COX-2 inhibitor 5 with benzimidazole scaffold

à higher activity than coxibs in cellular assay

à predicted binding mode similar to SC-558

but: no true scaffold-hop!

à new COX-2 inhibitor 5 with benzimidazole scaffold

à higher activity than coxibs in cellular assay

à predicted binding mode similar to SC-558

but: no true scaffold-hop!

Franke et al. (2005) J. Med. Chem. 48:6997.31

Why?

• Shape of the COX-2 binding pocket

• Diversity of reference ligands

à only 14 scaffolds (in 94 compounds)à biased reference data ?

“Mickey Mouse” scaffold

àààà “Privileged motif”

• Shape of the COX-2 binding pocket

à narrow, small, limited binding possibilitiesà “bad” choice of target ?

• Descriptor level of abstraction

à too “atomistic”, too fine-grained ?

32

LIQUIDLIQUIDLigand-based Quantification ofLigand-based Quantification of

Interaction Distributions

33

• Ligand-based Quantification of Interaction Distributions

• Fuzzy pharmacophore models based on trivariate Gaussians

“Fuzzy” Pharmacophores: LIQUID

34

The problem:Spherical PPPs are not appropriate for planar structures

à a solution:Trivariate Gaussians

( ) −21 µx

Univariate distribution

Trivariate distribution

Bivariate distribution

( )

σ

−−

πσ=

2

2

2exp

)2(

1)(

µxxunivG

( ) ( )

σ

−+

σ

−−

πσσ=

22

222

21

211

22

21

212

1exp

2

1),(

µxµxxxbivG

( ) ( )

Σ

−−−

πΣ=

µxµxx

T

3 2

1exp

)2(

1)(trivG

LIQUID step 1: Calculation of local feature density (LFD)

Atom typing:

{C![(N),(O)], S![(H), (N), (O)], Cl, I, Br}

{OH,NH}

{O,N![H]}

cluster radius(L: 4 Å, D/A: 1.9 Å)

N: # atoms of type „Type“

∑=

−=N

i c

Type

i

Type

kType

kr

atomatomDatomLFD

1

2 ),(1,0max)(

36

LIQUID step 2: Clustering of atom types

• Union-Find strategy

INIT: each atom is a singleton.

FOR each atom i of type T

FOR each atom j of type T

calculate Distance(i,j)

INIT: each atom is a singleton.

FOR each atom i of type T

FOR each atom j of type T

calculate Distance(i,j)

IF Distance ≤ ClusterRadius rc THEN

FIND maxLFD(Clusteri)

FIND maxLFD(Clusterj)

IF maxLFD(Clusteri)≤ maxLFD(Clusterj) THEN

UNION Clusterj with Clusteri

IF Distance ≤ ClusterRadius rc THEN

FIND maxLFD(Clusteri)

FIND maxLFD(Clusterj)

IF maxLFD(Clusteri)≤ maxLFD(Clusterj) THEN

UNION Clusterj with Clusteri

• number of final clusters depends on rc

37

LIQUID step 3: Calculation of cluster centroids

• Cluster ≡ PPP

= ∑∑∑

n

j

j

n

j

j

n

j

j

T

k zn

yn

xn

PPPc1

,1

,1

)(g

x, y, z: cartesian atom coordinates

jjj

38

LIQUID step 4: Principle Component Analysis

NIPALS

scaling of PCs bystandard deviation

à PC unit length

39

LIQUID step 5: Encoding as a correlation vector

OH

Type B

Type A

× bin

d

à alignment-free descriptor vectorA, B : PPP typesi, j : PPP instances

dmax = 20 Å

{ }ji

A

i

B

j

BA

dBApairs

CV trivGtrivG2

1

),(#

1,⋅⋅= ∑∑

40

A Tough Target: TAR RNA

G32 G33

U31 G34

C30 A35

C29 G36

G28 C37

A27 U38

G26 C39

Bulge

G26

C39

U25Acetylpromazine

a) b) c)

G26 C39

U25

C24

U23

A22 U40

G21 C41

A20 U42

C19 G43

C18 G44

G17 C45

G16 C46

U40

C24

1lvj, model 1(Du et al., 2002)

• very flexible target• only poor ligands known• “tough but typical”

41

b) c)

1LVJ

20 x Re-Docking

S

N

NH+

O

RNA Ligand Pharmacophores (1)

Docking ofTop 100

SPECS

DBCherry PickingFRET Assay

42

02

In vitro

transcription/translationTAR

N

O

O

HO O

N

O

O

N

HO

N = 13

Vendor lib.


N = 13

(95) (79) (52) (49) (49) (45) (39) (35) (34) (34)

a) Most prevalent scaffolds (top 2000 cpds.)

43

Cl Cl

NHO

HO

OH

NO2

Chloramphenicol

Cl

HN

S N

HO

O

OH

OH

OHH

H

H

S

O

O

N

O

OO

NO

N+

O-

O

N

O

O

Vendor lib.


Clindamycin

Tiamulin

Cl

N

O

S

HO

O

OH

H

30

TAR

N = 19

N

N

O

Cl

NN

NIn vitro

transcription/translation

44

AF2-Helix

Helix 3

Virtual Screening Strategy (PPARγγγγ Agonists)

Ser289

His449

Tyr473

His323

Farglitazar

AF2-Helix

Helix 3

O O

OH

EC50 = 15 µM (PPARγ)

1nyx: Ragaglitazar1knu: YPA1i7i: Tesaglitazar1zgy: Rosiglitazone

PhASTPhASTThe PharmacophoreThe Pharmacophore

Alignment Search Tool

PhAST

• PhAST – Pharmacophore Alignment Search Tool– Method for text-based comparison of molecules

– Uses a 2D-Pharmacophore Model

possible interactions PPP symbol

hydrogen bond acceptor Ahydrogen bond donor Dhydrogen bond donor Dcharge positive Pcharge negative Nlipophilic Laromatic Rhydrogen bond acceptor, hydrogen bond donor Ehydrogen bond acceptor, charge positive Qhydrogen bond acceptor, hydrogen bond donor, charge positive

U

no possible interactions O47

• Assign PPPs to atomsŁ 2D-PPP-Graph

• Create sequence of PPP-symbols– Canonical labeling of

S

N Cl

N

R

RR

R

RR

LR

RQ

RR

RR L

OL

OQ

O

O

OL

OQ

O

O

97

83

6

5

PhAST: The Concept

– Canonical labeling of vertices

– Combine vertex symbols following their indices

• Compare molecules

Ł compare sequences

LLQQOOLOORRRRRRRRRRRR

QORRLRRRROLRRRRQRROOL

QORRLRRRROLRRRRQLRROOL

Similarity score between0 – no similarity1 – identical molecules

R

RR

R

RR

LR

RQ

RR

RR L

OL

11

1012

18

2014

219

214

1513

1716 1

9

48

• Algorithm for canonical labeling: Weininger et al. (1988)

• Similarity between sequences X and Y (S(X,Y)) is computed by

– Pairwise global Sequence Alignment (Needleman & Wunsch, 1970)

PhAST: The Algorithms

– Pairwise global Sequence Alignment (Needleman & Wunsch, 1970)

)),((

)),((),(

YXAL

YXAMYXS =

A(X,Y): Alignment of X and YM(A(X,Y)): number of matches in A(X,Y)

L(A(X,Y)): length of A(X,Y)

PPP type count frequency

R 98,786 40.4%

P 325 0.1%

N 2,569 1.1%

PhAST:

PPP Frequencies in Drugs and Lead Structures

N 2,569 1.1%

E 6,572 2.7%

A 10,473 4.3%

U 9,025 3.7%

Q 5,933 2.4%

L 49,853 20.4%

O 60,969 24.9%

Data from COBRA 8.2

50

A E R L N O P Q UA 8 2 -4 -2 -1 -2 -4 4 -2E 12 -8 -4 -9 -4 -6 -4 0R 3 1 -4 -4 -5 -9 -13

L 2 -2 -2 -2 -4 -6

PhAST: Scoring Matrix

L 2 -2 -2 -2 -4 -6N 10 -2 -6 -7 -10O 2 -2 -4 -6P 10 6 4Q 14 6U 16

51

PhAST: Alignments & Alignment Scores

S

N

N

Cl

S

N

N

S

N

N EEU__LO_______RRRRRR

### || ||||||

LQQOOLOORRRRRRRRRRRR

Score: 3Similarity : 0.40

_LQQOOLOORRRRRRRRRRRR

||||||||||||||||||||




||||||||||||||||||||


Score: 76Similarity: 1.00

A

A B CHO

HO

NH2

S

N

N

Cl

S

N

N

S

N

N EEU__LO_______RRRRRR

### || ||||||



_LQQOOLOORRRRRRRRRRRR

||||||||||||||||||||




||||||||||||||||||||


Score: 76Similarity: 1.00

A

A B CHO

HO

NH2


|||||||||||||||||||||


Score: 78Similarity : 1.00S

N

N

Cl

S

EEU___LO_______RRRRRR

### || ||||||



EEULORRRRRR

|||||||||||

EEULORRRRRR


B

C

HO

HO

NH2


|||||||||||||||||||||


Score: 78Similarity : 1.00S

N

N

Cl

S

EEU___LO_______RRRRRR

### || ||||||



EEULORRRRRR

|||||||||||

EEULORRRRRR


B

C

HO

HO

NH2

52

ShapeletsShapelets

Shape-based similarity searchingShape-based similarity searching

53

Surface Shape Index

convex conacave

knob

ridge

saddle

cleft

bag

54

1. Approximate molecule as a sum of Gaussians

2. Extract an isosurface

3. Reduce the number of

“Shapelets”: Surface Decomposition

3. Reduce the number of points on the isosuface

4. Fit paraboloids

5. Origins of paraboloids are characteristic points

Proschak et al. (2008) J. Comp. Chem. 29:108

55

Shapelets Decomposition & Alignment

Molecule 1 Molecule 2

Clique detection inassociation graph

56

Excursion: Bron-Kerbosch Algorithm

for maximal Clique IdentificationC. Bron, J. Kerbosch (1973) Algorithm 457: Finding All Cliquesof an Undirected Graph. Communications of the ACM Vol. 16 (9), ACM Press: New York.

Implemented in: CLIP (Willett et al. 2003), Zhang & Grigorov 2006, Shapelets (Schneider et al. 2008)

Step 1:

Form the Correspondence Graph („Assiociation graph“, Form the Correspondence Graph („Assiociation graph“, „Product graph“) from the two molecular graphs.

Step 2:

Search for Cliques (= completely connected subgraph, which is not contained in any other completely connected subgraph) in the Correpondence Graph by “backtracking tree search” .

Cliques & Subgraphs

• complete subgraph of a graph: part of a graph in which all nodes are connected to each other

• cliques: maximal complete subgraphs (not subsumed by any other complete subgraph)

58

The Correspondence Graph, C

A1 A2 A3

A4

B1 B2 B3

B4

B5 A4,B1A4,B2A4,B3A4,B4A4,B5

A2,B1

A2,B2

A2,B3

A2,B4

A2,B5

A3,B1

A3,B2

A3,B3

A3,B4

A3,B5

A4,B1

A4,B2

A4,B3

A4,B4

A4,B5

A1,B1

A1,B2

A1,B3

A1,B4

A1,B5

C =

A2,B1 A2,B2 A2,B3 A2,B4 A2,B5

A3,B1

A3,B2

A3,B3

A3,B4

A3,B5A1,B1

A1,B2

A1,B3

A1,B4

A1,B5

etc.

Connect two nodes (AI, BX) and (AJ, BY)in C, if D(AI, AJ) = D(BX, BY).Connect two nodes (AI, BX) and (AJ, BY)in C, if D(AI, AJ) = D(BX, BY).

59

Treelevel List

Alreadyseen Candidates Action

C2 = (A1,B2)

C4 =(A2,B1)

C5 = (A3,B3)

C1 =(A1,B2)

Bron-Kerbosch Algorithm

C3 = (A2,B2)

(A2,B1) (A3,B3)(A1,B2)

A Correspondence Graph, C

60

Alignment with Shapelets

Alignment with Shapelets

Scoring withLIQUID

Scoring withLIQUID

Shapelets + LIQUID = SQUIRREL

61

SPECS ~200.000molecules

~3.000PPAR-ligand like

molecules

PPAR Agonists:

Virtual Screening with SQUIRREL (1)

SOM

60 candidates

FunctionalAssay21 molecules

Cherry Picking

62

SNN

COOH

N

Shape + Pharmacophore Screening: SQUIRREL

àààà new PPARαααα-selective scaffold

OH

EC50 PPARαααα = 0.044 ± 0.005 µMEC50 PPARγγγγ = 4.9 ± 0.4 µM

• Automated ligand docking with GOLD (Verdonk et al., 2003)

• Receptor structure 2p54, co-crystal with GW590735(Sierra et al., 2007)

For further reading ….

"This book provides a brilliant first access to the interdisciplinary field of molecular design. ... a ’must have’.“

Journal of Chemical Information and Modeling

“The authors have done an admirable job of“The authors have done an admirable job ofsimply explaining a complex and rapidly evolving field to a wide and varied audience.“

Journal of Medicinal Chemistry

Exercise

1) Go to the CATS website: www.modlab.de à software à CATSlight• make yourself familiar with the software options

2) Perform similarity searching in the provided data set (screeningCompounds.sdf)with this SMILES query structure:

CS(=O)(=O)c1ccc(cc1)n2nc(cc2c3ccc(F)cc3)C(F)(F)F

• How does the molecule look like in a 2D sketch?(à http://daylight.com/daycgi_tutorials/depict.cgi)

Change the various CATS parameter settings.• Do you see an influence on the resulting hit list?• How do you explain the differences?• Do you observe „scaffold hops“?

SO

O

N

N

F

F

F

F

OHS

NN

O

OHN

NHN

OO

1 2 3 4 5

The screening compounds

NH

S OO

NHNH

O NH

N

HO OH

O

NH

N

HO

OH

O

NHN

N

6 7 8 9 10

Lecture1 Pharmacophores & MolecularSimilarity · 2012-01-18 · Lecture1 Pharmacophores &...

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Transcript of Lecture1 Pharmacophores & MolecularSimilarity · 2012-01-18 · Lecture1 Pharmacophores &...