Indiana University School of David Wild – I533 2006. Page 1 David Wild [email protected] Chemical Informatics

David Wild – I533 2006. Page 1 Indiana University School of

David [email protected]

http://www.informatics.indiana.edu/djwild

Chemical Informatics tools, services

and workflows


Outline

• Chemical Informatics software packages available at IU

• Open source software • The need for integration & innovation• Pipelines, workflows and web services


Software at IUB Informatics

• Spotfire DecisionSite• ChemTK• ArgusLab• BCI software – cluster analysis, fingerprints,

Markush• OpenEye software – 3D conformer, docking• Chemaxon• gNova CHORD• Chemoinformatics programming toolkits

– Daylight, BCI, OpenEye


Open Source / Free Software

• Blue Obelisk - http://wiki.cubic.uni-koeln.de/dokuwiki/doku.php

• InChI - http://www.iupac.org/inchi/ • JMOL – http://jmol.sourceforge.net• FROWNS - http://frowns.sourceforge.net/• OpenBabel - http://openbabel.sourceforge.net/• CML - http://cml.sourceforge.net/• CDK - http://almost.cubic.uni-koeln.de/cdk/• MMTK -

http://starship.python.net/crew/hinsen/MMTK/


The need for integration

• Research computing is currently very fragmented• Existing approaches do not scale up to the amount of data now

common• Many chemical informatics tools are obscure, difficult to use and

access• Scientists’ questions are not that complex, but finding the answers

is currently very time consuming and/or complex (for a human)– “has anybody patented this chemical structure I just made?”– “can I get hold of a compound that might bind to the active site of this

protein I just resolved?”– “which compounds in this series are least likely to exhibit toxic

effects?”• Answers are often “stale” after a short period of time – questions

need to be re-answered as new information is generated• Almost all available systems are passive, and follow the

(web) browsing model• There tends to be one interface for every data source

(or encompassing just a few)


Oracle Database (HTS)

Compounds were tested against related assays and showed activity, including

selectivity within target families

Oracle Database (Genomics)

? None of these compounds have been tested in a

microarray assay

Computation

The information in the structures and known activity data is good enough to create

a QSAR model with a confidence of 75%

External Database (Patent)

Some structures with a similarity > 0.75 to these

appear to be covered by a patent held by a competitor

Computation

All the compounds pass the Lipinksi Rule of Five and

toxicity filters

Excel Spreadsheet (Toxicity)

One of the compounds was previously tested for

toxicology and was found to have no liver toxicity

Word Document (Chemistry)

Several of the compounds had been followed up in a

previous project, and solubility problems prevented further

development

Journal Article

A recent journal article reported the effectiveness of some compounds in a related series against a target in the same family

Word Document (Marketing)

A report by a team in Marketing casts doubt on

whether the market for this target is big enough to make development cost-effective

SCIENTIST

“These compounds look promising from their HTS results. Should I commit some

chemistry resources to following them up?”

?


Pipelining and workflow tools

• These tools permit applications to be “piped” together or connected in “workflows” where the output of one program can be given as input to another program (or script)

• Graphical front ends are replacing scripting – e.g. PERL, Python, etc

• Available graphical tools– Scitegic Pipeline Pilot - http://www.scitegic.com– Inforsense KDE - http://www.inforsense.com/– Taverna – http://taverna.sourceforge.net– IO-Informatics Sentient – http://www.io-informatics.com

• Find their real power in a web services environment





Web Services

• Semantic Web – “Next Big Thing”– Encode semantics in web pages (XML)– Describes services as well as information (SOAP, WSDL,

UDDI)– Computation detached from interface– Note seeping through to general web usage

• http://www.google.com/apis/• http://www.amazon.com/webservices

• eScience (UK)– £200m over 2001-2006 period– http://www.rcuk.ac.uk/escience/

• Cyber Infrastructure / Grid (US)– Semantic Web Health Care & Life Sciences Research

Group - http://www.w3.org/2001/sw/hcls/


CICC-related projects

• Formal CICC projects1. Innovative cross-screen analysis of NIH DTP Human Tumor Cell

Line Data – innovative scientific analysis of NIH HTS data2. Development of cheminformatics web services and use cases in

Taverna – web service & workflow infrastructure3. Development of a novel interface for the analysis of PubChem

HTS data – tools for interacting with lots of complex data4. A structure storage and searching system for Distributed Drug

Discovery – innovative kinds of chemical databases

• Other, related projects– Fast clustering of very large datasets using Linux clusters– Smart client for mining drug discovery data (Microsoft

supported)


PROJECT 4Experimental

Databases

PROJECT 2Web services& workflows PROJECT 1

Innovative cross-screenanalysis ofHTS data

PROJECT 3Visualization, navigation

& analysis tools forHTS data

SMART CLIENTSmart interfaces (incl.NLP, RSS, agents, etc)

SMART CLIENTGeneral drug discovery

web services& workflows SMART CLIENT

Smart interfaces (incl.NLP, RSS, agents, etc)

FAST PARALLELCLUSTERING

Using DivKmeans& AVIDD


Desired outcomes by Summer 2006

• A chemical informatics web service infrastructure running at IU• Several Taverna workflows that use these and other web

services, and which demonstrate that the infrastructure can be used to perform complex, relevant operations on PubChem data

• Demonstrated scientific results with the NIH DTP data• An established Distributed Drug Discovery database linked with

PubChem, that shows that our techniques together with PubChem can be employed in ways which benefit humanity in general

• A sandbox PubChem copy with improved functionality and architecture

• One or more novel visualization tools for PubChem data• Demonstrate the feasibility of fast, accurate clustering of very

large datasets (including the whole of PubChem) using the AVIDD Linux Cluster and a parallelized clustering algorithm (DivKmeans)

• Show that .NET and Java-based web services can work well together in a common infrastructure

• Demonstrate the feasibility of a natural language or other straightforward interface for scientists to express their information needs


NIH DatabaseService

PostgreSQLCHORD

FingerprintGenerator

BCI Makebits

ClusterAnalysis

BCI Divkmeans TableManagement

VoTables

PlotVisualizer

VoPlot

DockingSelector

Script

2D-3D

OpenEye OMEGA

Docking

OpenEye FRED

3D Visualizer

JMOL

Cluster the compounds in the NIH DTP database by chemical structure, then

choose representative compounds from the clusters and dock them into

PDB protein files of interest

SMILES + ID

Fingerprints

PDB DatabaseService

SMILES + ID + Data

ClusterMembership

SMILES + ID + + Cluster # + Data

SMILES + ID

MOL File

PDB Structure +

Box

Docked Complex


“However large an array of facts, however rapidly they accumulate,it is possible to keep them in order and to extract from

time to time digests containing the most generally significantinformation, while indicating how to find those items of

specialized interest. To do so, however, requires the willand the means”

“[we need to] get the best information in the minimum quantityin the shortest time, from the people who are producing theinformation to the people who want it, whether they know they

want it or not”

J.D. Bernal, quoted in Murray-Rust et. al., Org. Biomol. Chem., 2004, 2, 3192-3203


“Smart Client” for drug discovery

An open-source prototype that implements a new model of data mining that would, on request, “push” relevant information to pharmaceutical scientists in response to previously-defined straightforward expressions of needs, rather than relying on them stumbling upon the right information using traditional “browsing” models.

… using workflows and web services




Onlinedatabase

(e.g. PubChem)

Localdatabase

3D DockingTool

2D-3Dconverter

3Dvisualizer

UDDI

New Structure Service

Search online databasesfor recent structures

Search local databasesfor recent structures

Merge Results

AGENT / SMART CLIENT

Parse requestSelect appropriate use cases

and/or web service(s)Schedule as necessary

Request from Human Interface

WSDLSOAP

atomic services

aggregate services

USE-CASE SCRIPT

Invoke New Structure ServiceConvert structures to 3DDock results & protein file

Extract any hitsReturn links for visualization


Prototype development plan

• Develop a handful of use-cases based around industry/academia scientists

• Build 5-6 data / computation sources (e.g. enumeration, property calculation, structure database) that can fulfill the use cases

• Build WSDL and SOAP web services around the data sources that can be accessed from Taverna

• Develop workflows in Taverna (see taverna.sourceforge.net) • Publish web services in UDDI• Encode use-cases into scripts• Build Intelligent Agent / Smart Client node that can match user

needs with scripts & web services using workflows• Develop browser interface through Contextual Inquiry/Usability

Studies• Consider mapping to a Natural Language Interface


Use Case #1Are there any good ligands for my

target?• A chemist is working on a project involving a

particular protein target, and wants to know:– Any newly published compounds which might fit the

protein receptor site– Any published 3D structures of the protein or of protein-

ligand complexes– Any interactions of compounds with other proteins– Any information published on the protein target


Use Case #1Are there any good ligands for my

target?• A chemist is working on a project involving a

particular protein target, and wants to know:– Any newly published compounds which might fit the

protein receptor site gNova / PostgreSQL, PubChem search, FRED Docking

– Any published 3D structures of the protein or of protein-ligand complexes PDB search

– Any interactions of compounds with other proteins gNova / PostgreSQL, PubChem search

– Any information published on the protein target Journal text search


Use Case #2Who else is working on these

structures?• A chemist is working on a chemical series for a

particular project and wants to know:– If anyone publishes anything using the same or related

compounds– Any new compounds added to the corporate collection

which are similar or related – If any patents are submitted that might overlap the

compounds he is working on– Any pharmacological or toxicological results for those or

related compounds– The results for any other projects for which those

compounds were screened


Use Case #2Who else is working on these

structures?• A chemist is working on a chemical series for a

particular project and wants to know:– If anyone publishes anything using the same or related

compounds ~ PubChem search– Any new compounds added to the corporate collection

which are similar or related gNova CHORD / PostgreSQL– If any patents are submitted that might overlap the

compounds he is working on ~ BCI Markush handling software

– Any pharmacological or toxicological results for those or related compounds gNova CHORD / PostgreSQL, MiToolkit

– The results for any other projects for which those compounds were screened gNova CHORD / PostgreSQL, PubChem search


Priorities for web service development

• Search of PubChem– Wrap around HTTP or SOAP request

• Search of local gNova / PostgreSQL database– Wrap around application

• Molecular docking with OpenEye FRED– Wrap around application

• Property calculation with Molinspiration MiTools– Wrap around application

• PDB Search– Already implemented as EMBL web service

• BCI Markush search– Wrap around application

• Fast clustering of large datasets– Wrap around grid-based application

• Visualizations of datasets– Client and service development – VisualiSAR, Spotfire


Use Case - CICCWhich of these hits should I follow up?

• An MLI HTS experiment has produced 10,000 possible hits out of a screening set of 2m compounds. A chemist at another laboratory wants to know if there are any interesting active series she might want to pursue, based on:– Structure-activity relationships– Chemical and pharmacokinetic properties– Compound history– Patentability– Toxicity– Synthetic feasibility


Use Case – ECCRWhich of these hits should I follow up?

• An HTS experiment has produced 10,000 possible hits out of a screening set of 2m compounds. A chemist on the project wants to know what the most promising series of compounds for follow-up are, based on:– Series selection cluster analysis– Structure-activity relationships modal fingerprints/stigmata– Chemical and pharmacokinetic properties mitools,

chemaxon– Compound history gNova / PostgreSQL– Patentability BCI Markush handling software– Toxicity– Synthetic feasibility– + requires visualization tools!


Technology

• Perl SOAP::Lite – Will be used for initial web service development– Doesn’t really implement WSDL & UDDI

• Apache Axis & Tomcat– Deploy WSDL for web services

• BPEL4WS – Business Process Execution Language– For aggregation of web services– http://www-128.ibm.com/developerworks/library/

specification/ws-bpel/

• Microsoft .NET & C#


Current activities

• Core activities– Development of use-cases– Development of initial web services (Perl SOAP::Lite)– Use of Taverna to prototype use-case scripts

• Basic research on future components– Organizing large amounts of chemical information

for human consumption• Development of very fast parallel clustering techniques –

to be exposed as web services– Selection of interface-level tools for basic interaction

• Chemical structure drawing, display• Investigation of email, NLP, RSS, and browser interfaces

– Interface-level tools for visualization, navigation and analysis

• Cluster and dataset visualization, natural language interfaces)


Cluster Analysis and Chemical Informatics

• Used for organizing datasets into chemical series, to build predictive models, or to select representative compounds

• Organizational usage has not been as well studies as the other two, but see– Wild, D.J., Blankley, C.J. Comparison of 2D Fingerprint Types and

Hierarchy Level Selection Methods for Structural Grouping using Wards Clustering, Journal of Chemical Information and Computer Sciences., 2000, 40, 155-162.

• Essentially helping large datasets become manageable• Methods used:

– Jarvis-Patrick and variants• O(N2), single partition

– Ward’s method• Hierarchical, regarded as best, but at least O(N2)

– K-means• < O(N2), requires set no of clusters, a little “messy”

– Sphere-exclusion (Butina)• Fast, simple, similar to JP

– Kohonen network• Clusters arranged in 2D grid, ideal for visualization


Limitations of Ward’s method forlarge datasets (>1m)

• Best algorithms have O(N2) time requirement (RNN)

• Requires random access to fingerprints– hence substantial memory requirements (O(N))

• Problem of selection of best partition– can select desired number of clusters

• Easily hit 4GB memory addressing limit on 32 bit machines– Approximately 2m compounds


Scaling up clustering methods

• Parallelisation– Clustering algorithms can be adapted for multiple

processors– Some algorithms more appropriate than others for

particular architectures– Ward’s has been parallelized for shared memory

machines, but overhead considerable

• New methods and algorithms– Divisive (“bisecting”) K-means method– Hierarchical Divisive– Approx. O(NlogN)


Divisive K-means Clustering

• New hierarchical divisive method – Hierarchy built from top down, instead of bottom up– Divide complete dataset into two clusters– Continue dividing until all items are singletons– Each binary division done using K-means method– Originally proposed for document clustering

• “Bisecting K-means”– Steinbach, Karypis and Kumar (Univ. Minnesota)

http://www-users.cs.umn.edu/~karypis/publications/Papers/PDF/doccluster.pdf

– Found to be more effective than agglomerative methods– Forms more uniformly-sized clusters at given level


BCI Divkmeans

• Several options for detailed operation– Selection of next cluster for division– size, variance, diameter– affects selection of partitions from hierarchy, not shape of

hierarchy

• Options within each K-means division step – distance measure– choice of seeds– batch-mode or continuous update of centroids– termination criterion

• Have developed parallel version for Linux clusters / grids in conjunction with BCI

• For more information, see Barnard and Engels talks at: http://cisrg.shef.ac.uk/shef2004/conference.htm


Comparative execution timesNCI subsets, 2.2 GHz Intel Celeron processor

7h 27m

3h 06m

2h 25m

44m

0

5000

10000

15000

20000

25000

30000

0 20000 40000 60000 80000 100000 120000Number of Structures in Clustered Set

Exe

cutio

n T

ime

(s)

Wards

K-means

Divisive K-means

Parallel Divisive Kmeans (4-node)


Clustering a 1 million compound dataset

on a 2.2 GHz Celeron Desktop Machine

Method Time * Memory Usage

K-Means(10,000 clusters)

3½ days 95 MB

Divisive K-means 7 days 65 MB

Divisive K-means(Parallel, 4 machinesincl. 1.7 GHz Pentium M)

16½ hours

~ 50 MB

* Time for a single run may vary due to different selection of seeds. Runtimes can be shortened e.g. by using a max. number of iterations or a % relocation cutoff.

Results from AVIDD clusters & Teragrid coming soon….


Divisive Kmeans: Conclusions

• Much faster than Ward’s, speed comparable to K-means, suitable for very large datasets (millions) – Time requirements approximately O(N log N)– Current implementation can cluster 1m compounds in under

a week on a low-power desktop PC– Cluster 1m compounds in a few hours with a 4-node parallel

Linux cluster

• Better balance of cluster sizes than Wards or Kmeans• Visual inspection of clusters suggests better assembly of

compound series than other methods• Better clustering of actives together than previously-

studied methods• Memory requirements minimal• Experiments using AVIDD cluster and Teragrid

forthcoming(50+ nodes)


Visualization & interface level tools

• No matter how clever the smarts underneath, the overriding factor in usefulness will be the quality of scientists’ interaction with the system

• Contextual Design, Interaction Design (Cooper) and Usability Studies have proven effective in designing the right interfaces for the right peoplein chemical informatics [collaboration with HCI?]

• Possibility of multiple interfaces for different people groups(Cooper’s “primary personas”)

• Don’t assume the browser interface – email / NLP ?• Start with the basics

– 2D chemical structure drawing (input)– Visualization of large numbers of chemical structures in 2D– 3D chemical structure visualization

• Planning on evaluation of NLP, email, RSS, etc. as well asbrowser-based interfaces


Usability of 2D structure drawing tools

• Key difference between “sequential” and “random” drawers

• Huge difference in intuitiveness• Key factor how badly you can mess things up• Marvin Sketch ≈ JME > ChemDraw >> ISIS Draw


Visualization methods for datasets &

clusters• Partitions– Spreadsheets– Enhanced Spreadsheets– 2D or 3D plots

• Hierarchies– Dendograms– Tree Maps– Hyperbolic Maps




VisualiSAR – with a nod to Edward Tufte.See http://www.daylight.com/meetings/mug99/Wild/Mug99.html


Tree Maps – very Tufte-esque


3D Visualization - JMOL

Open Source, very flexible, works in a web service environment: jmol.sourceforge.net


Conclusions so far

• Effective exploitation of large volumes and diverse sources of chemical information is a critical problem to solve, with a potential huge impact on the drug discovery process

• Most information needs of chemists and drug discovery scientists are conceptually straightforward, but complex (for them) to implement

• All of the technology is now in place to implement may of these information need “use-cases”: the four level model using service-oriented architectures together with smart clients look like a neat way of doing this

• The aggregation and interface levels offer the most challenges• In conjunction with grid computing, rapid and effective organization and

visualization of large chemical datasets is feasible in a web service environment

• Some pieces are missing:– Chemical structure search of journals (wait for InChI)– Automated patent searching– Effective dataset organization– Effective interfaces, especially visualization of large numbers of 2D structures

(we’re working on it!)

Documents

Indiana University School of David Wild – I533 2006. Page 1 David Wild [email protected] Chemical Informatics