This document provides an overview of using the KNIME analytics platform for computational drug design. It introduces KNIME and its basic components, including nodes, workflows and dialog boxes. The document then describes a sample workflow for compound selection and focused screening, which involves reading chemical data, calculating properties, applying filters and selecting diverse molecules. The overall goal is to guide users through building a KNIME workflow to select compounds for further screening.

1. Day
4:
KNIME
Tutorial
George
Papadatos,
PhD
Francis
Atkinson,
PhD
ChEMBL
group

2. Outline
• Introduc>on
to
KNIME
• Basic
components
• Desktop,
nodes,
dialogs,
workflows
• Exercise
• Compound
selec>on
for
focused
screening
• Read
chemical
data
• Calculate
proper>es
• Apply
drug-­‐
and
lead-­‐
likeness
filters
• Remove
“nasty”
compounds
• Pick
diverse
molecules
• Visualize
results
and
plot
proper>es
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3. What
is
KNIME?
• KNIME
=
Konstanz
Informa>on
Miner
• Developed
at
University
of
Konstanz
in
Germany
• Desktop
version
available
free
of
charge
(Open
Source)
• Modular
plaWorm
for
building
and
execu>ng
workflows
using
predefined
components,
called
nodes
• Core
func>onality
available
for
tasks
such
as
standard
data
mining,
analysis
and
manipula>on
• Extra
features
and
func>onality
available
in
KNIME
through
extensions
from
various
groups
and
vendors
• WriYen
in
Java
based
on
the
Eclipse
SDK
plaWorm
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Design

4. KNIME
resources
• Web
pages
(documenta>on)
• www.knime.org
|
tech.knime.org
|
tech.knime.org/installa>on-­‐0
• Downloads
• knime.org/download-­‐desktop
• Community
forum
• tech.knime.org/forum
• KNIME
User
Training
Manual
• Books
and
white
papers
• knime.org/node/33079
• Myself
• georgep@ebi.ac.uk
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5. What
can
you
do
with
KNIME?
• Data
manipula>on
and
analysis
• File
&
database
I/O,
sor>ng,
filtering,
grouping,
joining,
pivo>ng
• Data
mining
/
machine
learning
• R,
WEKA,
interac>ve
plofng
• Chemoinforma>cs
• Conversions,
similarity,
clustering,
(Q)SAR
analysis,
reac>on
enumera>on
• Scrip>ng
integra>on
• R,
Perl,
Python,
Matlab,
Octave,
Groovy
• Repor>ng
• Much
more
• Bioinforma>cs,
image
analysis,
network
&
text
mining
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Drug
Design

6. Community
contributions
• hYp://tech.knime.org/community
• Chemoinforma>cs
• CDK
(EBI),
RDKit
(Novar>s),
Indigo
(GGA),
ErlWood
(Eli
Lilly),
Enalos
(NovaMechanics)
• Bioinforma>cs
• HCS
(MPI),
NGS
(Konstanz)
• Text
mining
• Palladian
• Integra>on
• Python,
Perl,
R,
Groovy,
Matlab
(MPI),
PDB
web
services
client
(Vernalis)
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Drug
Design

7. Installation
&
updates
• Download
and
unzip
KNIME
• No
further
setup
required
• Addi>onal
nodes
aker
first
launch
• knime.ini
contains
arguments
&
parameters
for
launch
• New
sokware
(nodes)
from
update
sites
• hYp://tech.knime.org/update/community-­‐contribu>ons/release
• Workflows
and
data
are
stored
in
a
workspace
• /Users/georgep/knime/workspace_mac_new
• C:knime_2.5.4workspace
• Customiza>on
in:
FileàPreferencesàKNIME
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8. Auto-­‐layout
Execute
Execute
all
nodes
KNIME
Workbench
Node
descrip>on
tabs
workflow
projects
favorite
nodes
public
server
workflow
editor
node
repository
outline
console
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Computa5onal
Drug
Design

9. KNIME
nodes:
Overview
Node
=
basic
processing
unit
of
KNIME
workflow
which
performs
a
par>cular
task
Input
port(s)
–
on
the
lek
of
icon
Title
Output
port(s)
–
on
the
right
of
icon
Icon
Status
display
(‘traffic
lights’)
Right-­‐click
menu
Sequence
number
• Red
(not
ready)
To
configure
and
• Amber
(ready)
execute
the
node,
• Green
(executed)
display
the
output
views,
edit
the
• Blue
bar
during
execu>on
node,
and
display
(with
percentage
or
flashing)
data
for
the
ports
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Computa5onal
Drug
Design

10. KNIME
nodes:
Dialogs
Double
click
to
configure…
Configura>on
menus
for
selected
nodes
Explicit
column
type
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Drug
Design

11. An
example
completed
workGlow
• Workflows
can
be
imported
and
exported
as
.zip
files
• With
or
without
the
underlying
data
• File
à
Import
KNIME
workflow…
• File
à
Export
KNIME
workflow…
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12. Any
questions
so
far?
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Drug
Design

13. Compound
selection
for
focused
screening
1. Read
chemical
data
2. Remove
duplicates
• Iden>ty
ensured
by
InChi
keys
3. Filter
out
compounds
in
ChEMBL
• Iden>ty
ensured
by
InChI
keys
4. Calculate
phys/chem
proper>es
5. Apply
drug-­‐
and
lead-­‐likeness
filters
6. Apply
more
filters
(e.g.
remove
solubility
liabili>es)
7. Apply
substructural
filters
(PAINS
subset)
8. Pick
diverse
molecules
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14. Your
objective
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Design

15. First
steps
-­‐
I
• Locate
the
directory
with
today’s
material
1
2
• Copy
and
paste
it
to
your
desktop
• You
can
take
it
with
you
too
• Open
the
presenta>on
file
• Import
the
FocusedScreeningSelec>on.zip
to
KNIME
• Menu
à
File
à
Import
workflow
to
KNIME
3
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Design

16. First
steps
-­‐
II
• Open
a
new
workflow
• Right
click
on
the
workflow
projects
area
1
2
3
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Drug
Design

17. Part
1:
Reading
and
cleaning
up
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Drug
Design

18. SDF
Reader
.dataSMDC_cleaned.sdf
1 3
4
2 5
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Drug
Design

19. Inspect
the
structures…
Right
click
on
the
node
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Drug
Design

20. GroupBy
1
3
2
5
4
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Computa5onal
Drug
Design

21. GroupBy
Example
Name Course Grade
George German 68
George Maths 86
George Physics 99
Group
by
Name
and
Group
by
Name
and
then
take
first
row
then
average
Grade
Name Course (first) Grade (first) Name Grade (avg.)
George German 68 George 84.33
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Drug
Design

22. File
Reader
1
.dataall_human_chembl.csv
2 3
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Computa5onal
Drug
Design

23. Reference
Row
Filter
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Drug
Design

24. Molecule
to
RDKit
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Drug
Design

25. Any
questions
so
far?
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Drug
Design

26. Part
2:
Property-­‐based
Giltering
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Drug
Design

27. Descriptor
Calculation
1
2
3
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Drug
Design

28. Java
Snippet
1 .codeLipinski.txt
3
2
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Drug
Design

29. Numeric
Row
Splitter
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Drug
Design

30. Inspect
the
Lipinski
fails…
Right
click
on
the
node
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Drug
Design

31. Java
Snippet
1 .codeOprea.txt
3
2
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Drug
Design

32. Numeric
Row
Splitter
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for
Computa5onal
Drug
Design

33. Inspect
the
Oprea
fails…
Right
click
on
the
node
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for
Computa5onal
Drug
Design

34. Numeric
Row
Splitter
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for
Computa5onal
Drug
Design

35. Inspect
the
Solubility
fails…
Right
click
on
the
node
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for
Computa5onal
Drug
Design

36. Any
questions
so
far?
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Drug
Design

37. Part
3:
Substructure-­‐based
Giltering
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Drug
Design

38. Molecule
to
Indigo
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for
Computa5onal
Drug
Design

39. File
reader
.dataPAINS_clean_half.sdf
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Drug
Design

40. Query
Molecule
to
Indigo
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Drug
Design

41. Inspect
the
SMARTS
rules
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for
Computa5onal
Drug
Design

42. Chunk
Loop
Start
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Drug
Design

43. Substructure
Matcher
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Drug
Design

44. Loop
End
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for
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Drug
Design

45. Inspect
matched
structures…
Right
click
on
the
node
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for
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Drug
Design

46. Reference
Row
Filter
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Drug
Design

47. Any
questions
so
far?
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Drug
Design

48. Part
4:
Diversity
picking
and
plotting
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Drug
Design

49. RDKit
Fingerprint
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Drug
Design

50. Inspect
the
Gingerprints…
Right
click
on
the
node
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for
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Drug
Design

51. RDKit
Diversity
Picker
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Drug
Design

52. 2D/3D
Scatterplot
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for
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Drug
Design

53. Inspect
the
plot…
Right
click
on
the
node
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Drug
Design

54. Any
questions
so
far?
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Drug
Design

55. Conclusions
• Compound
selec>on
for
focused
screening
• Theory
and
prac>ce
• Typical
scenario
• KNIME
• Open
and
free
• Chemoinforma>cs
toolkits
• Erl
Wood,
RDKit
and
Indigo
• Not
perfect
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Drug
Design

56. Further
reading
• Open
data
and
tools
1. A freeJ. J.; Sterling, T.; Mysinger, M. M.; Bolstad, E. S.; Coleman, R. G., ZINC:
Irwin,
tool to discover chemistry for biology. Journal of Chemical Information
and Modeling 2012 ASAP.
2. Saubern, S.; Guha, R.; Baell, J. B., KNIME workflow to assess PAINS filters in
SMARTS format. Comparison of RDKit and Indigo cheminformatics libraries.
Molecular Informatics 2011, 30, (10), 847-850.
3. Barnes, M. R.; Harland, L.; Foord, S. M.; Hall, M. D.; Dix, I.; Thomas, S.;
Williams-Jones, B. I.; Brouwer, C. R., Lowering industry firewalls: pre-
competitive informatics initiatives in drug discovery. Nature Reviews Drug
Discovery 2009, 8, (9), 701-708.
4. Berthold, M. R.; Cebron, N.; Dill, F.; Gabriel, T. R.; Kötter, T.; Meinl, T.; Ohl, P.;
Sieb, C.; Thiel, K.; Wiswedel, B., KNIME: The Konstanz Information Miner. In
Data Analysis, Machine Learning and Applications, Preisach, C.; Burkhardt, H.;
Schmidt-Thieme, L.; Decker, R., Eds. Springer: Berlin, 2008; pp 319-326.
5. Tiwari, A.; Sekhar, A. K. T., Workflow based framework for life science
informatics. Computational Biology and Chemistry 2007, 31, (5-6), 305-319.
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Drug
Design

57. Further
reading
• High
throughput
screening
1. Bajorath, J., Integration of virtual and high-throughput screening. Nature
Reviews Drug Discovery 2002, 1, (11), 882-894.
2. Harper, G.; Pickett, S. D.; Green, D. V. S., Design of a compound
screening collection for use in High Throughput Screening. Combinatorial
Chemistry & High Throughput Screening 2004, 7, (1), 63-70.
• Lead-­‐
and
drug-­‐likeness
1. Chuprina, A.; Lukin, O.; Demoiseaux, R.; Buzko, A.; Shivanyuk, A., Drug- and
lead-likeness, target class, and molecular diversity analysis of 7.9 million
commercially available organic compounds provided by 29 suppliers. Journal of
Chemical Information and Modeling 2010, 50, (4), 470-479.
2. Lipinski, C. A., Lead- and drug-like compounds: the rule-of-five revolution. Drug
Discovery Today: Technologies 2004, 1, (4), 337-341.
3. Oprea, T. I.; Davis, A. M.; Teague, S. J.; Leeson, P. D., Is there a difference
between leads and drugs? A historical perspective. Journal of Chemical
Information and Computer Sciences 2001, 41, (5), 1308-1315.
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Design

58. Further
reading
• Physicochemical
proper>es
and
drug
discovery
1. Brüstle, M.; Beck, B.; Schindler, T.; King, W.; Mitchell, T.; Clark, T., Descriptors,
physical properties, and drug-likeness. Journal of Medicinal Chemistry 2002, 45,
(16), 3345-3355.
2. Hill, A. P.; Young, R. J., Getting physical in drug discovery: A contemporary
perspective on solubility and hydrophobicity. Drug Discovery Today 2010, 15,
(15/16), 648-655.
3. Leeson, P. D.; Springthorpe, B., The influence of drug-like concepts on decision-
making in medicinal chemistry. Nature Reviews Drug Discovery 2007, 6, (11),
881-890.
• Structural
alerts
in
HTS
1. Baell, J. B.; Holloway, G. A., New substructure filters for removal of Pan Assay
Interference Compounds (PAINS) from screening libraries and for their exclusion in
bioassays. Journal of Medicinal Chemistry 2010, 53, (7), 2719-2740.
2. Rishton, G. M., Reactive compounds and in vitro false positives in HTS. Drug
Discovery Today 1997, 2, (9), 382-384.
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59. Further
reading
• Similarity
and
diversity
1. Ashton, M.; Barnard, J.; Casset, F.; Charlton, M.; Downs, G.; Gorse, D.; Holliday,
J.; Lahana, R.; Willett, P., Identification of diverse database subsets using
property-based and fragment-based molecular descriptions. Quantitative
Structure-Activity Relationships 2002, 21, (6), 598-604.
2. Bender, A.; Glen, R. C., Molecular similarity: a key technique in molecular
informatics. Organic and Biomolecular Chemistry 2004, 2, 3204-3218.
3. Gorse, A.-D., Diversity in medicinal chemistry space. Current Topics in Medicinal
Chemistry 2006, 6, (1), 3-18.
4. Maldonado, A.; Doucet, J.; Petitjean, M.; Fan, B.-T., Molecular similarity and
diversity in chemoinformatics: From theory to applications. Molecular Diversity
2006, 10, (1), 39-79.
5. Rogers, D.; Hahn, M., Extended-connectivity fingerprints. Journal of Chemical
Information and Modeling 2010, 50, (5), 742-754.
6. Schuffenhauer, A.; Brown, N., Chemical diversity and biological activity. Drug
Discovery Today: Technologies 2006, 3, (4), 387-395.
7. Willett, P.; Barnard, J. M.; Downs, G. M., Chemical similarity searching. Journal
of Chemical Information and Computer Sciences 1998, 38, (6), 983-996.
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Design

60. Day
4:
KNIME
Tutorial
George
Papadatos,
PhD
Francis
Atkinson,
PhD
ChEMBL
group