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When Command Line Tools meet KNIME:

Using the best of the two worlds to support

drug discovery teams

Man-Ling Lee 250th ACS National Meeting 08/17/2015 Workflow Tools & Data Pipelining in Drug Discovery Symposium

M. Lee 250th ACS National Meeting 08/17/2015

What this talk is about 2

Disclaimer This talk is not about application user interfaces!

Motivation It’s about Genentech’s approach of building an

extensible and sustainable infrastructure to support drug discovery teams.


Three Example Applications 3

Project Vortex Sessions ü Easy-to-use command line tools enable comp chemists to take

ownership of project database ü KNIME/Command Line tool integration to facilitate the maintenance

of the Unix pipes

DMPK Model Validation ü Easy-to-understand predicted probabilities for prioritizing

compounds in a “local” context ü KNIME/Command Line tool integration to enable comp chemists to

build high-performance end-user applications

Fragment Hit Clustering ü Easy-to-visualize AAPSim/SE clustering supports project teams in hit

follow-up processes ü Use command Lines tools to build new Command Line tools


Genentech’s Way of Accessing Project Data 4

Comp chemists provide Vortex sessions to their project teams


Command Line Tools @ Genentech 5

Autocorrelator package A. Gobbi, M. Lardy, M. Lee http://code.google.com/p/autocorrelator/

Aestel package M. Lee, A. Gobbi http://sourceforge.net/projects/aestel/

Genentech package A. Gobbi, J. Feng, M. Lee, E. Kochetkova, B. Seller, K. Clark

Data Manipulation Chemical Structure Properties Diversity sdfTagTool.csh sdfMDLSSSMatcher.csh sdf2Omega.py sdfCFP.csh

sdf2Tab.csh sdfEnumerator.csh sdfFastRocs.pl sdfFingerprinter.csh

sdf2Xls.csh sdfTransformer.csh sdfCalcProps.py sdfCluster.pl

sdfAggregator.csh sdfRGroupExtractor.pl sdfRGroupCalcProps.pl sdfFPCluster.pl

sdfBinning.csh sdfRingSystemExtraction.csh sdfCNS_MPO.grvy sdfFPNNFinder.csh

sdfDataPivoter.csh sdfSmarsGrep.csh sdfGiniCalculator.csh sdfMCSSNNFinder.csh

sdfFromImage.py sdfStructureTagger.csh sdfSelectivityCalculator.csh sdfFPSphereExclusion.csh

sdfSdfMerger.csh sdfSubRMSD.csh sdfLE.grvy sdfMCSSSphereExclusion.csh

sdfSplicer.csh sdfNormalizer.csh sdfTorsionScanner.csh

sdfSliceByRe.pl sdfAlign.csh / sdf2DAlign.csh sdfConformerSampler.csh

sdfSorter.csh Database Modeling Other sdfTabMerger.csh AEREAExporter.csh sdfRRandomForestCreator sdfGroovy.csh

sdfMolSeparator.csh dataLoader.csh sdfRSVMCreator sdfMultiplexer.pl

sdfGrapheme.csh sdfExport.pl sdfRModelPredictor sdfIMatch.csh

tab2sdf.csh sdfSdfExporter.csh sdfMosaicFromSMDI.pl tabTabMerger.pl tabExport.pl


AEREA UI for Configuring Exports from Database 6

Specify compounds of interest

+ Display data of interest

Components Commercial Compounds


AEREA UI for Configuring Exports from Database 7

Table report with compounds and data of interest

A

K

J

I

H

G

F

E

D

C

B

Hewitt et. al. JCIM, 2005


AEREA Command Line for Automating Data Retrieval 8

AEREAExporter.csh -uName manle \ -treeName smdiRes \

-hitlistLevel "Base Compound" \ -qName “demo_searchQuery" \ -rName “demo_reportTemplate” \ -out .sdf

J AEREA enables users to retrieve data from the database J No need to know about SQL and data model J The data export function can be called from command line


An Unix Pipe Example 9

( AEREAExporter.csh -‐out .sdf -‐hitlistLevel "Base Compound" -‐uName manle \ -‐qName "project cmpds" -‐rName “report" -‐treeName smdiRes \

;AEREAExporter.csh -‐out .sdf -‐hitlistLevel "Base Compound" -‐uName albertgo \ -‐qName "tested in assay" -‐rName “report" -‐treeName smdiRes \

) | sdfTagTool.csh -‐in .sdf -‐out .sdf -‐rmRepeatTag 'G-‐Number=1‘ \ | sdfTagTool.csh -‐in .sdf -‐out .sdf -‐rename rename_list.txt \ | sdfGiniCalculator.csh -‐in .sdf -‐out .sdf -‐idField "G-‐Number" -‐conc 1 \ | sdfStructureTagger.csh -‐in .sdf -‐out .sdf -‐smarts projectCore_SMARTS.tab \

-‐sets projectCores -‐tag_info "firstTag" \

| sdfSelectivityCalculator.csh -‐in .sdf -‐out .sdf \ -‐denominator "Target-‐1 Ki" –numerator "Target-‐2 Ki"\

-‐outputMode separate -‐selectivity "Target-‐1/Target-‐2" \

| sdfGroovy.csh -‐in .sdf -‐out .sdf –f calcLigandEfficiency.grvy \ | sdfTabMerger.csh -‐sdf .sdf –out .sdf \ -‐tab $userDump/PK_data.tab -‐mergeMode multiRecordKeepTemplate \

-‐mergeTag "G-‐Number" -‐mergeCol "G-‐Number“ -‐quiet \

| sdfTagTool.csh -‐in .sdf -‐out.sdf -‐reorder reorder_list.txt \ > projectVortex_SAR.sdf


“Mis”-using KNIME to debug UNIX pipes 10


KNIME’s Dynamic Node Generation Framework … 11


… and the specification of Command Line Tools … 12

XML file with definition of the command line programs •  Generate one node per <command> element •  Deduce ports from <ports> element •  Nodes are initialized during startup of KNIME Desktop

<commands> <config> <exchangeDir local='\\resfiles….' remote='/gnet/…'/> <ssh remoteHost='rescomp2' timeout='1000' initFileTemplate='~cdduser/bin/knimerc.$mode'/> </config> <command name='AEREAExporter.csh'> <IO out="-out .sdf"/> <default>-hitlistLevel 'Base Compound' -uName XXXX -rName 'XVortex Project Subst‘ ….</default> <ports out='sdf'/> </command> <command name='sdfGrep.pl'> <IO in="" out=""/> <default>-i GNum</default> <ports in='sdf' out='sdf'/> </command> …


… for generating Command Line nodes 13

Display the “compiled” Unix command up to

the current node



Project Vortex Sessions ü Task-oriented command line tools enable comp chemists to take

ownership of project databases ü KNIME/Command Line tool integration to facilitate the maintenance

of the Unix pipes







Assessing the risks, increasing the success rate 15

Regression

Predicted probability

Predicted Value

Classifica4on threshold Cumula&ve Distribu&on:

Given mean and SD we can describe a Normal Distribu4on Func4on

From regression to classifica&on Create a regression model, then compute the probability of the predicted value being below/above the set threshold

Aliagas et. al. JCAMD, 2015

When prioritizing compounds, chemists are interested in picking the “winners”

Predicted probability is easy to use and widely applicable


“Adjusting” Global Models For Local Use 16

cPPB_prob_bin

cPPB_prob is the predicted probability of the compound being > 95% bound to plasma protein

Compound prioritization ü Chemistry teams decide which probability cut-off to use ü Cut-off could be different for different series of compounds


PPB Model Validation Application 17

Collaboration with I. Aliagas, A. Gobbi

Users can evaluate the quality of models or

which probability cut-off to use

Project 1 Project 2 Project 3 Project 4 Project 6 Project 7 Project 8

none

Project 1

Cmpd ID_____none Project 1

DMPK_Validation/123445678_345678/

Cmpd ID


PPB Model Validation- KNIME Workflow 18

GUI GUI Compute


Node Template: GenerateValidationData 19

Performance Boost Running the computation in parallel on Linux cluster

Predict

Calculate Properties


PPB Model Validation- KNIME Workflow 20

GUI GUI Compute


Access DMPK Model Validation From Vortex 21

Automatically generate the validation files on daily basis for • download from Vortex menu • sharing with partners if requested

Collaboration with I. Aliagas, F. Broccatelli





of the Unix pipes







Cluster fragment hits and visualizing results 23

The results from Atom-Atom-Path Similarity / Sphere Exclusion clustering can be effectively visualized in scatterplots

Gobbi et. al. J of Cheminformatics, 2015

M. Lee 250th ACS National Meeting 08/17/2015 Gobbi et. al. J of Cheminformatics, 2015

Watch out for structure relationships ! 24


Atom-Atom-Path Similarity: A MCSS-based similarity 27

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1 Compare the atom paths of

all pairs of atoms

Calculate the molecular similarity from the atomic similarities of the aligned atoms

Align atom pairs according to the atomic similarities



Hudson et. al. QSAR, 1996

Lee et. al. JCISCS, 2003

Sphere Exclusion-based Clustering 28

• Sort the compounds by ligand efficiency (or any other properties of interest)

• Select the diverse sub set (of cluster seeds)

• Assign excluded molecules to the most similar cluster seed


Sphere Exclusion-based Clustering – Behind the scene 29





of the Unix pipes




Fragment Hit Clustering ü Properties-biased AAPSim/SE clustering for supporting project teams

in hit follow-up processes ü Use Command Lines tools to build new Command Line tools


Program to create UNIX pipes, a pipe dream? 31

sdfTagTool.csh -copy TITLE=___sdfCalcProps_saved_title___ -addCounter -counterTag ___sdfCalcProps_counter___ \ -title ___sdfCalcProps_counter___ -in .smi -out .sdf \ | tee /tmp/sdfCalcProps.$$.1437358230757.orig.sdf \ | sdfFilter.csh -in .sdf -out .sdf \ | filter -in .sdf -out .sdf -dots false -filter /dev/null -pkanorm false -prefix /tmp/filter.$USER.$$ \ | sdfTagTool.csh -in .sdf -out .sdf -keep ___sdfCalcProps_counter___ > /tmp/sdfCalcProps.$$.1437358230757.filtered.sdf; echo NCCO \ | babel -in .smi -out .sdf >> /tmp/sdfCalcProps.$$.1437358230757.filtered.sdf; cat /tmp/sdfCalcProps.$$.1437358230757.filtered.sdf \ | blabber.py \ | OEProps.csh -in .sdf -out .sdf H_polar Charge \ | sdf2Tab.csh -in .sdf -tags "___sdfCalcProps_counter___|Charge|H_polar" \ | sdfTabMerger.csh -tab - -sdf /tmp/sdfCalcProps.$$.1437358230757.filtered.sdf \ -mergeTag ___sdfCalcProps_counter___ -mergeCol ___sdfCalcProps_counter___ -out .sdf \ | cMR.py \ | MoKa1.py --cLogD_74 --cLogD_all --cLogP \ | sdfALogP.csh -in .sdf -out .sdf -outputCounts \ | sdfNormalizer.csh -in .sdf -out .sdf -shortMessage \ | sdfGroovy.csh -in .sdf -out .sdf \ -c '$>c_pKa_MB_model = $c_pKa_MB.length()==0 ? 0 : $c_pKa_MB;$>c_pKa_MA_model = $c_pKa_MA.length()==0 ? 14 : $c_pKa_MA;‘ \ | sdfTopologicalIndexer.csh -in .sdf -out .sdf Zagreb Wiener JStar JYStar JXStar JY JX \ | sdfCFP.csh -type functional -in .sdf -out .sdf -format counts -nbits 256 -level 2 \ | OEProps.csh -in .sdf -out .sdf Solubility_Index CNS_MPO HeteroAliphaticRings CarboAliphaticRings AliphaticRings HeteroAromaticRings \ CarboAromaticRings AromaticFraction RO5 RotBonds Rings Heavy_Atoms N+O AromaticRings TPSA MW NH+OH \ | sdfSdfExport.csh -in .sdf -out .sdf -queryTags CTISMILES -sqlFile $AESTEL_DIR/config/exporter/models/models.xml -sqlName mLogD \ | sdfGroovy.csh -in .sdf -out .sdf -c '$>LogD74_TypeInModel="C"; $>LogD74_in_Model=tVal($mol,"cLogD7.4");' \ | sdfRModelPredictor.pl -in .sdf -out .sdf \ -modelLocation ModelData/DMPK/PPB/201412/models/PPB_H_SVM_201412 -modelName PPB_H_SVM_201412 \ | sdf2Tab.csh -in .sdf -tags "___sdfCalcProps_counter___|cPPB_H|cPPB_H_err|cPPB_H_fu|cPPB_H_pct|cPPB_H_prob|cPPB_H_version" | sdfTabMerger.csh -sdf /tmp/sdfCalcProps.$$.1437358230757.orig.sdf -tab - \ -mergeTag ___sdfCalcProps_counter___ -mergeCol ___sdfCalcProps_counter___ -out .sdf \ | sdfTagTool.csh -in .sdf -title ___sdfCalcProps_saved_title___ -out .sdf \ | sdfTagTool.csh -in .sdf -remove "___sdfCalcProps_counter___\ | ___sdfCalcProps_saved_title___" -out .sdf

echo CC | sdfCalcProps.csh -in .smi –out .sdf cPPB_H

generates the two UNIX commands


Calculator dependencies: cPPB Example 32

cPPB_H

pChem

counts

ALogPCounts

c_pKa

Solubility_Index

JX …

FFP2_256

cLogD7.4

AromaticRings

Dependencies are encoded in xml files


Calculator dependencies: cPPB Example 33

cPPB_H

pChem

counts

ALogPCounts

c_pKa

Solubility_Index

JX …

FFP2_256

cLogD7.4

AromaticRings

Start with the execution of independent calculators


The growing Calculator Warehouse 34

AliphaticRings AromaticFraction AromaticRings.

CarboAliphaticRings CarboAromaticFraction CarboAromaticRings

Charge cMR

H_polar: Heavy_Atoms

HeteroAliphaticRings HeteroAromaticRings

MW N+O

NH+OH RotBonds

AFP1_128 AFP1_256

AFP2 AFP2_128 AFP2_256 AFP3_128 AFP3_256 FFP1_128 FFP1_256 FFP2_128 FFP2_256 FFP3_128 FFP3_256

MACCSKeys

TopoIndexes EStateCount EStateSum

cBrainPerm_score cCYP_Inh

cHep cLM

cMDCK-MDRI_AB cMDCK-MDRI_efflux

cMDCK_AB cMDCK_efflux

cPPB cTDI

pChem cKinSol

Solubility_Index ALogP

ALogPCounts cLogP c_pKa

cgLogD cLogD7.4 cLogD_all mLogD7.4

CNS_MPO RO5

HTSCysReactivity HTSExclusion HTSNeverSee

cIC50atLE0.3


Acknowledgements 35

Computer Drug Design Group Ignacio Aliagas

Jeff Blaney Fabio Broccatelli

Huifen Chen Kevin Clark JW Feng

Alberto Gobbi Chinchih Lu Ben Sellers

Members of CDD Groups

Roche Pharma IT Simran Hansrai

Elena Kochetkova Slaton Lipscomb

Barry Pon Hubert Pun

KNIME.com Michael Berthold Thomas Gabriel

Bernd Wieswedel


Project Vortex Sessions – Behind the Scene 37

Cron job (internal)

Scripts for Vortex file generation

Files for Vortex filesToCopy

unknownFiles (internal)

File shares

Cron job (file share)

Copy Instruction

Pulls files to the specified file

shares

S. Hansrai, H. Pun, B. Pon

Internal use External use


… equals Genentech Command Line Nodes 38

Collaboration with A. Gobbi, T. Gabriel, B. Wiswedel

J  “Command Line” Node Categories:

• Reader

• Processor

• Writer

• Utility

J  “Command Line” ports handle • Data in SD file format • Unix command text


Straightforward Command Line Input … 39

Copy and paste the text from the Node Description window


Accessing PPB Model Validation Application 40

KNIME WebPortal

Application Dashboard

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