Post on 19-Jun-2020
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
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
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
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
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
3-Point Pharmacophore Screening:
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
?
3-Point Pharmacophore Screening:
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.
RNA Ligand Pharmacophores (2)
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.
RNA Ligand Pharmacophores (3)
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
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
||||||||||||||||||||
LLQQOOLOORRRRRRRRRRRR
Score: 71Similarity : 0.95
LQQOOLOORRRRRRRRRRRR
||||||||||||||||||||
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: 3Similarity : 0.40
_LQQOOLOORRRRRRRRRRRR
||||||||||||||||||||
LLQQOOLOORRRRRRRRRRRR
Score: 71Similarity : 0.95
LQQOOLOORRRRRRRRRRRR
||||||||||||||||||||
LQQOOLOORRRRRRRRRRRR
Score: 76Similarity: 1.00
A
A B CHO
HO
NH2
LLQQOOLOORRRRRRRRRRRR
|||||||||||||||||||||
LLQQOOLOORRRRRRRRRRRR
Score: 78Similarity : 1.00S
N
N
Cl
S
EEU___LO_______RRRRRR
### || ||||||
LLQQOOLOORRRRRRRRRRRR
Score: 2Similarity : 0.38
EEULORRRRRR
|||||||||||
EEULORRRRRR
Score: 62Similarity : 1.00
B
C
HO
HO
NH2
LLQQOOLOORRRRRRRRRRRR
|||||||||||||||||||||
LLQQOOLOORRRRRRRRRRRR
Score: 78Similarity : 1.00S
N
N
Cl
S
EEU___LO_______RRRRRR
### || ||||||
LLQQOOLOORRRRRRRRRRRR
Score: 2Similarity : 0.38
EEULORRRRRR
|||||||||||
EEULORRRRRR
Score: 62Similarity : 1.00
B
C
HO
HO
NH2
52
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
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“?