Building a Chemical Informatics Grid

Marlon Pierce

Community Grids Laboratory

Indiana University

Acknowledgments

CICC researchers and developers who contributed to this presentation: Prof. Geoffrey Fox, Prof. David Wild, Prof. Mookie Baik,

Prof. Gary Wiggins, Dr. Jungkee Kim, Dr. Rajarshi Guha, Sima Patel, Smitha Ajay, Xiao Dong

Thanks also to Prof. Peter Murray Rust and the WWMM group at Cambridge University

More info: www.chembiogrid.org and www.chembiogrid.org/wiki.

Chemical Informatics and the Grid

An overview of the basic problem and solution

Chemical Informatics as a Grid Application Chemical Informatics is the application of information technology

to problems in chemistry. Example problems: managing data in large scale drug discovery

and molecular modeling Building Blocks: Chemical Informatics Resources:

Chemical databases maintained by various groups NIH PubChem, NIH DTP

Application codes (both commercial and open source) Data mining, clustering Quantum chemistry and molecular modeling

Visualization tools Web resources: journal articles, etc.

A Chemical Informatics Grid will need to integrate these into a common, loosely coupled, distributed computing environment.

Problem: Connecting It Together The problem is defining an architecture for tying all of

these pieces into a distributed computing system. A “Grid”

How can I combine application codes, web resources, and databases to solve a particular problem that interests me? Specifically, how do I build a runtime environment that can

connect the distributed services I need to solve an interesting problem?

For academic and government researchers, how can I do all of this in an open fashion? Data and services can come from anywhere That is, I must avoid proprietary infrastructure.

NIH Roadmap for Medical Researchhttp://nihroadmap.nih.gov/ The NIH recognizes chemical and biological

information management as critical to medical research.

Federally funded high throughput screening centers. 100-200 HTS assays per year on small molecules. 100,000’s of small molecules analyzed Data published, publicly available through NIH PubChem

online database. What do you do with all of this data?

High-Throughput Screening

Testing perhaps millions of compounds in a corporate collection to see if any show activity against a certain disease protein

High-Throughput Screening

Traditionally, small numbers of compounds were tested for a particular project or therapeutic area

About 10 years ago, technology developed that enabled large numbers of compounds to be assayed quickly

High-throughput screening can now test 100,000 compounds a day for activity against a protein target

Maybe tens of thousands of these compounds will show some activity for the protein

The chemist needs to intelligently select the 2 - 3 classes of compounds that show the most promise for being drugs to follow-up

Informatics Implications

Need to be able to store chemical structure and biological data for millions of data points Computational representation of 2D structure

Need to be able to organize thousands of active compounds into meaningful groups Group similar structures together and relate to activity

Need to learn as much information as possible(data mining) Apply statistical methods to the structures and related

information Need to use molecular modeling to gain direct

chemical insight into reactions.

The Solution, Part I: Web Services Web Services provide the means for wrapping

databases, applications, web scavengers, etc, with programming interfaces. WSDL definitions define how to write clients to talk with

databases, applications, etc. Web Service messaging through SOAP Discovery services such as UDDI, MDS, and so on.

Many toolkits available Axis, .NET, gSOAP, SOAP::Lite, etc.

Web Services can be combined with each other into workflows Workflow==use case scenario More about this later.

Basic Architectures: Servlets/CGI and Web Services

Browser

WebServer

HTTP GET/POST

DB

JDBC

WebServer

DB

JDBC

Browser

WebServer

SOAP

GUIClient

SOAPWSDL

WSDL

WSD

LWSD

L

Solution Part II: Grid Resources Many Grid tools provide powerful backend services

Globus: uniform, secure access to computing resources (like TeraGrid) File management, resource allocation management, etc.

Condor: job scheduling on computer clusters and collections

SRB: data grid access OGSA-DAI: uniform Grid interface to databases.

These have Web Service as well as other interfaces (or equivalently, protocols).

Solution, Part III: Domain Specific Tools and Standards -->More Services For Chemical Informatics, we have a number of tools and

standards. Chemical string representations

SMILES, InChI Chemistry Markup Language

XML language for describing, exchanging data. JUMBO 5: a CML parser and library

Glue Tools and Applications Chemistry Development Kit (CDK) OpenBabel

These are the basis for building interoperable Chemical Informatics Web Services

Analogous situations exist for other domains Astronomy, Geosciences, Biology/Bioinformatics

Solution Part IV: Workflows

Workflow engines allow you to connect services together into interesting composite applications.

This allows you to directly encode your scientific use case scenario as a graph of interacting services.

There are many workflow tools We’ll briefly cover these later. General guidance is to build web services first and then

use workflow tools on top of these services. Don’t get married to a particular workflow technology yet,

unless someone pays you.

Solution Part V: User Interfaces Web Services allow you to cleanly separate user

interfaces from backend services. Model-view-controller pattern for web applications

Client environments include Grid and web service scripting environments Desktop tools like Taverna and Kepler Portlet-based Web portal systems

Typically, desktop tools like Taverna are used by power users to define interesting workflows.

Portals are for running canned workflows.

Next steps

Next we will review the online data base resources that are available to us.

Databases come in two varieties Journal databases Data databases

As we will discuss, it is useful to build services and workflows for automatically interacting with both types.

Online Chemical Journal and Data Resources

MEDLINE: Online Journal Database MEDLINE (Medical Literature Analysis and Retrieval

System Online) is an international literature database of life sciences and biomedical information.

It covers the fields of medicine, nursing, dentistry, veterinary medicine, and health care.

MEDLINE covers much of the literature in biology and biochemistry, and fields with no direct medical connection, such as molecular evolution.

It is accessed via PubMed.

http://en.wikipedia.org/wiki/Medline

PubMed: Journal Search Engine PubMed is a free search engine offered by the United States National Library of Medicine as part of the Entrez information retrieval system.

The PubMed service allows searching the MEDLINE database. MEDLINE covers over 4,800 journals published in the United States

and more than 70 other countries primarily from 1966 to the present.

In addition to MEDLINE, PubMed also offers access to: OLDMEDLINE for pre-1966 citations. Citations to articles that are out-of-scope (e.g., general science and

chemistry) from certain MEDLINE journals In-process citations which provide a record for an article before it is

indexed with MeSH and added to MEDLINE Citations that precede the date that a journal was selected for

MEDLINE indexing Some life science journals

http://www.ncbi.nlm.nih.gov/entrez/query/static/overview.html

PubChem: Chemical Database PubChem is a database of chemical molecules. The system is maintained by the

National Center for Biotechnology Information (NCBI) which belongs to the United States National Institutes of Health (NIH).

PubChem can be accessed for free through a web user interface. And Web Services for programmatic access

PubChem contains mostly small molecules with a molecular mass below 500.

Anyone can contribute The database is free to use, but it is not curated, so value of a

specific compound information could be questionable. NIH funded HTS results are (intended to be) available through

pubchem.

http://pubchem.ncbi.nlm.nih.gov/

NIH DTP Database

Part of NIH’s Developmental Therapeutics Program.

Screens up to 3,000 compounds per year for potential anticancer activity.

Utilizes 59 different human tumor cell lines, representing leukemia, melanoma and cancers of the lung, colon, brain, ovary, breast, prostate, and kidney.

DTP screening results are part of PubChem and also available as a separate database.

http://dtp.nci.nih.gov/

Example screening results. Positive results (red bar to right of vertical line) indicates greater than average toxicity of cell line to tested agent.

http://dtp.nci.nih.gov/docs/compare/compare.html

DTP and COMPARE

COMPARE is an algorithm for mining DTP result data to find and rank order compounds with similar DTP screening results.

Why COMPARE? Discovered compounds may be less toxic to

humans but just as effective against cancer cell lines.

May be much easier/safer to manufacture. May be a guide to deeper understanding of

experiments

http://dtp.nci.nih.gov/docs/compare/compare_methodology.html

Many Other Online Databases Complementary protein information

Indiana University: Varuna project Discussed in this presentation

University of Michigan: Binding MOAD “Mother of All Databases” Largest curated database of protein-ligand complexes Subset of protein databank Prof. Heather Carlson

University of Michigan: PDBBind Provides a collection of experimentally measured binding affinity

data (Kd, Ki, and IC50) exclusively for the protein-ligand complexes available in the Protein Data Bank (PDB)

Dr. Shaomeng Wang

The Point Is… All of these databases can be accessed on line with

human-usable interfaces. But that’s not so important for our purposes

More importantly, many of them are beginning to define Web Service interfaces that let other programs interact with them. Plenty of tools and libraries can simulate browsers, so you

can also build your own service. This allows us to remotely analyze databases with

clustering and other applications without modifying the databases themselves.

Can be combined with text mining tools and web robots to find out who else is working in the area.

Encoding chemistry

Chemical Machine Languages Interestingly, chemistry has defined three simple

languages for encoding chemical information. InChI, SMILES, CML Can generate these by hand or automatically

InChIs and SMILES can represent molecules as a single string/character array. Useful as keys for databases and for search queries in

Google. You can convert between SMILES and InChIs

OpenBabel, OELib, JOELib CML is an XML format, and more verbose, but

benefits from XML community tools

SMILES: Simplified Molecular Input Line Entry Specification Language for describing the structure of

chemical molecules using ASCII strings.

http://www.daylight.com/dayhtml/doc/theory/theory.smiles.html

InChI: International Chemical Identifier IUPAC and NIST Standard similar to SMILES Encodes structural information about compounds Based on open an standard and algorithms.

http://wwmm.ch.cam.ac.uk/inchifaq/

InChI in Public Chemistry Databases US National Institute of Standards and Technology (NIST) - 150,000

structures NIH/NCBI/PubChem project - >3.2 million structures Thomson ISI - 2+ million structures US National Cancer Institute(NCI) Database - 23+ million structures US Environmental Protection Agency(EPA)-DSSToX Database - 1450

structures Kyoto Encyclopaedia of Genes and Genomes (KEGG) database - 9584

structures University of California at San Francisco ZINC - >3.3 million structures BRENDA enzyme information system (University of Cologne) - 36,000

structures Chemical Entities of Biological Interest (ChEBI) database of the European

Bioinformatics Institute - 5000 structures University of California Carcinogenic Potency Project - 1447 structures Compendium of Pesticide Common Names - 1437 (2005-03-03) structures

Journals and Software Using InChI Journals

Nature Chemical Biology. Beilstein Journal of Organic Chemistry

Software ACD/Labs ACD/ChemSketch. ChemAxon Marvin. SciTegic Pipeline Pilot. CACTVS Chemoinformatics Toolkit by Xemistry, GmbH.

http://wwmm.ch.cam.ac.uk/inchifaq/

Chemistry Markup Language

CML is an XML markup language for encoding chemical information. Developed by Peter Murray Rust, Henry Rzepa and others. Actually dates from the SGML days before XML

More verbose than InChI and SMILES But inherits XML schema, namespaces, parsers, XPATH,

language binding tools like XML Beans, etc. Not limited to structural information Has OpenBabel support.

http://cml.sourceforge.net/, http://cml.sourceforge.net/wiki/index.php/Main_Page

InChI Compared to SMILES SMILES is proprietary and

different algorithms can give different results.

Seven different unique SMILES for caffeine on Web sites: [c]1([n+]([CH3])[c]([c]2([c]([n+]1[CH3])

[n][cH][n+]2[CH3]))[O-])[O-] CN1C(=O)N(C)C(=O)C(N(C)C=N2)=C1

2 Cn1cnc2n(C)c(=O)n(C)c(=O)c12 Cn1cnc2c1c(=O)n(C)c(=O)n2C N1(C)C(=O)N(C)C2=C(C1=O)N(C)C=N

2 O=C1C2=C(N=CN2C)N(C(=O)N1C)C CN1C=NC2=C1C(=O)N(C)C(=O)N2C

On the other hand, some claimSMILES are more intuitive for human readers.

http://wwmm.ch.cam.ac.uk/inchifaq/

A CML Example

http://www.medicalcomputing.net/xml_biosciences.html

Clustering Techniques, Computing Requirements, and Clustering Services

Computational techniques for organizing data

The Story So Far

We’ve discussed managing screening assay output as the key problem we face Must sift through mountains of data in PubChem

and DTP to find interesting compounds. NIH funded High Throughput Screening will make

this very important in the near future. Need now a way to organize and analyze the

data.

Clustering and Data Analysis Clustering is a technique that can be applied to large data sets to

find similarities Popular technique in chemical informatics

Data sets are segmented into groups (clusters) in which members of the same cluster are similar to each other.

Clustering is distinct from classification, There are no pre-determined characteristics used to define the

membership of a cluster, Although items in the same cluster are likely to have many

characteristics in common. Clustering can be applied to chemical structures, for example, in

the screening of combinatorial or Markush compound libraries in the quest for new active pharmaceuticals.

We also note that these techniques are fairly primitive More interesting clustering techniques exist but apparently are not

well known by the chemical informatics community.

Non-Hierarchical Clustering Clusters form around centroids. The number of which can be specified by the user. All clusters rank equally and there is no particular

relationship between them.

http://www.digitalchemistry.co.uk/prod_clustering.html

Hierarchical Clustering Clusters are arranged in hierarchies

Smaller clusters are contained within larger ones; the bottom of the hierarchy consists of individual objects in "singleton" clusters, while the top of it consists of one cluster containing all the objects in the dataset.

Such hierarchies can be built either from the bottom up (agglomerative) or the top downwards (divisive)

http://www.digitalchemistry.co.uk/prod_clustering.html

Fingerprinting and Dictionaries--What Is Your Parameter Space? Clustering algorithms require a parameter space

Clusters defined along coordinate axes. Coordinate axes defined by a dictionary of chemical

structures. Use binary on/off for fingerprinting a particular compound

against a dictionary.

http://www.digitalchemistry.co.uk/prod_fingerprint.html

Cluster Analysis and Chemical Informatics Used for organizing datasets into chemical series, to build

predictive models, or to select representative compounds Clustering Methods

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 Parallelization

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)

Divisive K-means: 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)

Conclusions 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 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

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

Divisive K-Means as a Web Service The previous exercise was intended to show

that Divisive K-Means is a classic example of Grid application. Needs to be parallelized Should run on TeraGrid

How do you make this into a service? We’ll go on a small tour before getting back

to our problem.

Wrapping Science Applications as Services Science Grid services typically must wrap legacy

applications written in C or Fortran. You must handle such problems as

Specifying several input and output files These may need to be staged in

Launching executables and monitoring their progress. Specifying environment variables

Often these have also shell scripts to do some miscellaneous tasks.

How do you convert this to WSDL? Or (equivalently) how do you automatically generate the

XML job description for WS-GRAM?

Generic Service Toolkit (GFAC)(G. Kandaswamy, IU and RENCI) The Generic Service Toolkit can "wrap" any command-line

application as an application service. Given a set of input parameters, it runs the application, monitors

the application and returns the results. Requires no modification to program code. Also has web user interface generating tools.

When a user accesses an application service, the user is presented with a graphical user interface (GUI) to that service.

The GUI contains a list of operations that the user is allowed to invoke on that service.

After choosing an operation, the user is presented with a GUI for that operation, which allows the user to specify all the input parameters to that operation. The user can then invoke the operation on the service and get the output

results.

www.extreme.indiana.edu/gfac/

OPAL (S. Krishan, SDSC)

Features include scheduling (using Globus and Condor/SGE) and security (using GSI-based certificates), and persistent state management.

The WSDL defines operations to do the following: getAppMetadata: includes usage information, arbitrary application-specific

metadata specified as an array of other elements, e.g. description of the various options that are passed to the application

binary. launchJob: runs job with specified input and returns a Job ID. queryStatus: returns status code, message, and URL of the working

directory getOutputs: returns the outputs from a job that is identified by a Job ID.

URLs for the standard output and error Array of structures representing the output file names and URLs

getOutputAsBase64ByName: This operation returns the contents of an output file as Base64 binary.

destroy: This operation destroys a running job identified by a Job ID. launchJobBlocking: This operation requires the list of arguments as a string,

and an array of structures representing the input files.

http://grid-devel.sdsc.edu/gridsphere/gridsphere?cid=nbcrws

Our Solution: Apache Ant Services We’ve found using Apache Ant to be very useful for

wrapping services. Can call executables, set environment variables. Lots of useful built-in shell-like tasks. Extensible (write your own tasks). Develop build scripts to run your application

You can easily call Ant from other Java programs. So just write a wrapper service We use both blocking (hold connection until return) and

non-blocking version (suitable for long running codes). In non-blocking case, “Context” web service is used for

callbacks.

Flow Chart of SMILES to Cluster Partitioned of BCI Web ServiceSMILEString

Makebits

Dictionary(Default)

Fingerprint(*.scn)

DivKmeansCluster

Hierarchy(*.dkm)

Optclus RNNclusOne

ColumnProcess

MergeProcess

ExtractedCluster

Hierarchy(*.clu)

NewSMILEString

GeneratingFingerprints

ClusteringFingerprints

Generatingthe best levels

SMILES to DKM

Extracting individualcluster partitions

best

level

BCI Clustering Service Methods

Service Method Description Input Output

makebitsGenerate Generate fingerprints from a SMILES structure

SMIstring Fingerprint string

divkmGenerate Cluster fingerprints with Divkmeans

SCNstring Clustered Hierarchy

smile2dkm Makebits + divkm SMIstring Clustered Hierarchy

optclusGenerate Generate the best levels in a hierarchy

DKMstring Best partition cluster level

rnnclusGenerate Extract individual cluster partitions

DKMstring Indiv. cluster partitions

smile2ClusterPartitioned

Generate a new SMILES structure w/ extra col.

SMIstring New SMILES structure

A Library of Chemical Informatics Web Services

All Services Great and Small

Like most Grids, a Chemical Informatics Grid will have the classic styles: Data Grid Services: these provide access to data sources

like PubChem, etc. Execution Grid Services: used for running cluster analysis

programs, molecular modeling codes, etc, on TeraGrid and similar places.

But we also need many additional services Handling format conversions (InChI<->SMILES) Shipping and manipulating tabular data Determining toxicity of compounds Generating batch 2D images

So one of our core activities is “build lots of services”

VOTables: Handling Tabular Data Developed by the Virtual Observatory community for encoding

astronomy data. The VOTable format is an XML representation of the tabular

data (data coming from BCI, NIH DTP databases, and so on). VOTables-compatible tools have been built

We just inherit them. SAVOT and JAVOT JAVA Parser APIs for VOTable allow us

to easily build VOTable-based applications Web Services Spread sheet Plotting applications.

VOPlot and TopCat are two

Document Structure of VOTable

Compound

Name

Cluster

Number

Acemetacin 1

Candesartan 1

Acenocoumarol 2

Dicumarol 2

Phenprocoumon 2

Trioxsalen 2

Warfarin 2

<?xml version="1.0"?>

<VOTABLE version="1.1“ xmlns:xsi=http://www.w3.org/2001/XMLSchema-instance xsi:noNamespaceSchemaLocation="http://www.ivoa.net/xml/VOTable/VOTable/v1.1">

<RESOURCE >

<TABLE name="results">

<FIELD name=“CompoundName" ID="col1" datatype=“char" arraysize=“*”/>

<FIELD name=“ClustureNumber” ID="col2“ datatype=“int”/>

<DATA>

<TABLEDATA>

<TR><TD>Acemetacin</TD><TD>1</TD</TR>

<TR><TD>Candesartan</TD><TD>1</TD></TR>

<TR><TD>Acenocoumarol</TD><TD>2</TD></TR>

<TR><TD>Dicumarol</TD><TD>2</TD></TR>

<TR><TD>Phenprocoumon</TD><TD>2</TD></TR>

<TR><TD>Trioxsaken</TD><TD>2</TD></TR>

<TR><TD>warfarin</TD><TD>2</TD></TR>

</TABLEDATA>

</DATA>

</TABLE>

</RESOURCE>

</VOTABLE>

mrtd1.txt – smiles representation of chemical compounds along with its properties

Taverna Client

WSDL VOTableGeneratorService retrieveVOTableDocument

Tomcat Server

VOTableGeneratorService

mrtd1.txt

votable.xml VOPlot

Votable.xml : xml representation of mrtd1.txt file

VOPlot Application from generated votable.xml file : Graph plotted on Mass (X–axis) and PSA (Y-axis)

Other Uses for VOTables

VOTables is a useful intermediate format for exchanging data between data bases.

Simple example: exchange data between VARUNA databases. Each student in the Baik group maintains his/her on copy

(sandbox purposes). Often need to import/export individual data sets.

It is also good for storing intermediate results in workflows. Value is not the format, but the fact that the XML can be

manipulated programmatically. Unions, subset, intersection operations

More Services: WWMM ServicesServices Descriptions Input Output

InChIGoogle Search an InChI structure through Google

inchiBasic

type

Search result in HTML format

InChIServer Generate InChI version

format

An InChI structure

OpenBabelServer

Transform a chemical format to another using Open Babel

format

inputData

outputData

options

Converted chemical structure string

CMLRSSServer

Generate CMLRSS feed from CML data

mol, title description link, source

Converted CMLRSS feed of CML data

CDK-Based Services

Common Substructure

Calculates the common substructure between two molecules.

CDKsim Takes two SMILES and evaluates the Tanimoto coefficient (ratio of intersection to union of their fingerprints).

CDKdesc Calculates a variety of molecular and atomic descriptors for QSAR modeling

CDKws Fingerprint generation

CDKsdg Creates a jpeg of the compound’s 2D structure

CDKStruct3D Generates 3D coordinates of a molecule from its SMILE

ToxTree Service The Threshold of

Toxicological Concern (TTC) establishes a level of exposure for all chemicals below which there would be no appreciable risk to human health.

ToxTree implements the Cramer Decision Tree approach to estimate TTC.

We have converted this into a service. Uses SMILES as input. Note the GUI must be

separated from the library to be a service

http://ecb.jrc.it/QSAR/home.php?CONTENU=/QSAR/qsar_tools/qsar_tools_toxtree.php

toxTree

Taverna Workflow for Toxic Hazard Estimation

OSCAR3 Service Oscar3 is a tool for shallow, chemistry-specific

natural language parsing of chemical documents (i.e. journal articles).

It identifies (or attempts to identify): Chemical names: singular nouns, plurals, verbs etc., also

formulae and acronyms. Chemical data: Spectra, melting/boiling point, yield etc. in

experimental sections. Other entities: Things like N(5)-C(3) and so on.

There is a larger effort, SciBorg, in this area http://www.cl.cam.ac.uk/~aac10/escience/sciborg.html

This (like ToxTree) is potentially productively pleasingly parallelized.

It also has potentially very interesting Workflows

http://wwmm.ch.cam.ac.uk/wikis/wwmm/index.php/Oscar3

Use Cases and Workflows

Putting data and clustering together in a distributed environment.

Chemical Informatics as a Grid Problem NIH-Funded experimental screening

NIH DTP and HTS projects are generating a wealth of raw data on small compounds.

Available in PubChem Journal and chemical data sources often have public Web clients

and GUIs. But we need Web Service interfaces, not just Web interfaces. These provide a programming interfaces for building both human

and machine clients. These need to be connected to computing resources for running

clustering, data mining, and molecular modeling applications. Excellent candidates for running on the TeraGrid

We can formulate scientific problems that map to inter-connections of Grid services. This is generally called “Grid workflow” or “Service Orchestration”

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?”

?

Workflow, Services, and Science Web Services work best as simple stateless

services. No implicit input, output, or interdependency of

methods. Services must be composed into interesting

applications. This is called workflow. A good workflow ...

Is composed of independent services Completely specifies an interesting science

problem.

Some Open Source Grid Workflow Projects UK e-Science Project’s Taverna

Scufl.xml scripting, GUI interface, works with Web Services. Kepler

Works with Web services and the Globus Toolkit. Condor DAGMan

www.cs.wisc.edu/condor Works over the top of Condor’s scheduler. Extended by the GriPhyN Virtual Data System

Java CoGKit’s Karajan XML workflow specification for scripting COG clients. Works with GT 2 and 4.

Community Grids Lab’s HPSearch www.hpsearch.org JavaScript scripting, works with Web services.

Indiana Extreme Lab’s Workflow Composer www.extreme.indiana.edu/xgws/xwf/index.html Jython, BPEL (soon) scripting

Finding compound-protein relationships A protein implicated in tumor growth is

supplied to the docking program (in this case HSP90 taken from the PDB 1Y4 complex)

The workflow employs our local NIH DTP database

service to search 200,000 compounds tested in human

tumor cellular assays for similar structures to the

ligand. Client portlets are used to browse these

structures

Once docking is complete, the user visualizes the high-scoring docked structures in a portlet using the JMOL applet.

Similar structures are filtered for drugability, and are automatically passed to the OpenEye FRED docking program for docking into the target protein.

A 2D structure is supplied for input into the similarity search (in this case, the extracted bound ligand from the PDB IY4 complex)

Correlation of docking results and “biological fingerprints” across the human tumor cell lines can help identify potential mechanisms of action of DTP compounds

HTS data organization & flagging

A tumor cell line is selected. The activity results for all the compounds in the DTP database in the given range are extracted from the PostgreSQL database

The compounds are clustered on chemical structure

similarity, to group similar compounds together

The compounds along with property and cluster information are converted to VOTABLES format and displayed in VOPLOT

OpenEye FILTER is used to calculate biological and chemical properties of the compounds that are related to their potential effectiveness as drugs

Use Case: Which 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!

A Workflow Scenario: HTS Data Organization and Flagging This workflow demonstrates how screening data can be flagged

and organized for human analysis. The compounds and data values for a particular screen are

retrieved from the NIH DTP database and then are filtered to remove compounds with reactive groups, etc. A tumor cell line is selected. The activity results for all the

compounds in the DTP database in the given range are extracted from the PostgreSQL database

OpenEye FILTER is used to calculate biological and chemical properties of the compounds that are related to their potential effectiveness as drugs

ToxTree is used to flag the potential toxicities of compounds. Divkmeans is used to add a column of cluster numbers. Finally, the results are visualized using VOPlot and the 2D

viewer applet.

Web Services

Example plots of our workflow output using VOPlot and VOTables

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

Use Case: Are 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: Who 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

VARUNA – Towards a Grid-based Molecular Modeling Environment

A brief overview of Prof. Mookie Baik’s VARUNA project.

Chemical Informatics in Academic Research? Industrial Research: Target

Oriented Not bound to a specific

molecular system Not bound to a method Not concerned with

generality Aware of Efficiency Aware of Overall Cost Aware of Toxicity Concerned about

Formulations Cares about active

MOLECULES

Academic Research: Concept Oriented Specialized on few

molecular families Method Development is

important Obsessed with generality Does not care much about

efficiency Cost is unimportant Often can’t even assess for

Toxicity Formulation is a minor issue Cares mostly about

REACTIONS, i.e.ways to GET to a molecule

AutoGeFF, Varuna and Workflows Metalloproteins are extremely important in

biochemical processes Understanding their chemistry is difficult To add value to the small molecule DB’s

(PubChem, etc.), we must somehow connect them to PDB’s, BindMOAD, etc.

By extending Varuna’s functionality to handling, storing Metalloproteins, we could provide a connection

Automatic Generator of ForceFields (AutoGeFF) Developing a service that can take ANY

drug-like molecule (from PubChem, for example) metal complexes metalloenzymes (from PDB, for example) unnatural or functionalized amino acids, nucleobases (from in-

house db)for which molecular mechanics force fields are not available andautomatically generate FF’s based on High level Quantum Simulations (using Varuna as a Web

service)for Sophisticated Molecular Mechanics Simulations

First Step: Coding of a specialized Prototype that can reproduce our manually derived novel force fields for Cu-A Alzheimer’s Disease as a Proof-Of-Principles Study.

Automatic Quantum Mechanical Curation of Structure Data Chemical Research logic is often driven by molecular

structure Large-scale, small molecule DB’s (such as PubChem)

have low-resolution structure data Often key properties are not consistently available:

e.g.: Rotation-barriers, Redox Potentials, Polarizabilities, IR frequencies, reactivity towards nucleophiles

QM web-services will provide tools for generating high-resolution data that will curate the results of traditional ChemInfo studies allow for combinatorial computational chemistry access a database of modeling data

Prototype-Project: Controlling the TGF pathway

PDB

1IAS1IASInactive TGF

VARUNA

Experimentsin the Zhang

Lab

Active TGFActive TGFWith inhibitorWith inhibitor

PubChem

in-house Molecules in Varuna

Conceptual Conceptual Understanding of Understanding of TGFTGF

InhibitionInhibition

SimulationsAutoGeFFAutoGeFF

Questions:

- What molecular feature controls inhibitor binding?

- How do mutations impact binding?

Consequences for ChemInfo Design for Academia TWO Strategies are needed: Making traditional ChemInfo tools that are often available in

commercial research available to Academia is in principle straightforward.

New ChemInfo Tools that are CONCEPT centered and include REACTIONS in addition to MOLECULES must be developed.

Our approach: Development of

(a) Quantum Chemical Database(b) Molecular Modeling Database

Harness the power of recent advances in Molecular Modeling (QM, QM/MM, MM, MD) through information management.

Data-depository for Quantum Chemical Data including both Properties & Mechanisms

QM Calculation Workflow

XYZ File ofa Molecule Input File

Generator

Input Param

Varuna

Job SchedulingService

List ofComputers

Job ScriptService

SSHService

Supercomputers

More Information

Contact me: mpierce@cs.indiana.edu Most of this was taken from our CICC project. See

www.chembiogrid.org/wiki. Note we’ve found wikis to be extremely useful and fun to

use for maintaining collaborative web sites. See also www.crisisgrid.org and www.gorerle.com/vlab-wiki

for other examples using Media Wiki. Many elements of our approach are based on Prof.

Peter Murray Rust’s group’s approach. WWMM Wiki: wwmm.ch.cam.ac.uk/wikis/wwmm/index.php

SourceForge Project Site http://sourceforge.net/projects/cicc-grid

Additional Slides

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

CICC Web Services I

BCI Clustering Provides Bernard Chemical Information (BCI) clustering packages A module of the workflow for HTS data organization and flagging Status:

Added URL output support to the previous solid prototype (Multi-user durable) Taverna Beanshell Scripting for data format adjusting (e.g. Filtering out the head part listing

column names) To do: Evaluating the URI(URL) based workflow design

ToxTree Estimates toxic hazard by applying a decision tree approach A module of the workflow for HTS data organization and flagging Status: A test prototype producing the level of toxicity in a brief or verbose explanation

against a SMILE structure To do:

Refining the Web service for cluster input and external property support The Taverna Beanshell scripting for data merging not used in some modules

CICC Web Services II

Workflow for HTS data organization and flagging Demonstrates how screening data can be flagged and organized for human

analysis Status: Individual modules except the visualization are in prototype To do:

Defining at least XML schema or DTD for the workflow data (at most the Ontology) Redefining current workflow model to reflect the new feature of Taverna 1.4

supporting complex data structures and the provenance plugin Other Planed Web Services

Open Source Chemistry Analysis Routines (OSCAR) Extracts chemical information from text and produces an XML instance highlighting

the chemical information A module of the PMR workflow Status: OSCAR3 is available and works fine as a Java application To do: Studying XML instances for extracting chemical names

InfoChem’s SPRESI Web Service Provides access to the SPRESI molecule database Status: Perl scripts for accessing SPRESI Web Service To do: Developing a Web service wrapper to utilize InfoChem’s SPRESI Web

Service

BCI Clustering URL Service Methods

Service Method Description Input URLOutput

makebitsURLGenerate

Generate fingerprints from a SMILES structure

SMIstring Fingerprint and program output

divkmURLGenerate

Cluster fingerprints with Divkmeans

SCNstring DKM data and program output

smile2dkmURL Makebits + divkm SMIstring All SMI, DKM and std. outputs

optclusURLGenerate

Generate the best levels in a hierarchy

SMIstring

DKMstring

Best data and program output

rnnclusURLGenerate

Extract individual cluster partitions

SMIstring

DKMstring

New partition and std. output

smile2ClusterPartitionedURL

Generate a new SMILES structure w/ extra col.

SMIstring All intermediate data and output

Workflow for

smile2ClusterPartitionedURL

Workflow for Toxic Hazard in Verbose

Diagram of Workflow2

Input/Output

Web Services

Beanshell Scripting

Informatics

Informatics is the discipline of science which investigates the structure and properties (not specific content) of scientific information, as well as the regularities of scientific information activity, its theory, history, methodology and organization. The purpose of informatics consists in developing optimal methods and means of presentation (recording), collection, analytical-synthetic processing, storage, retrieval and dissemination of scientific information.

A. I. Mikhailov, A. I. Chernyi, R. S. Gilyarevskii (1967) “Informatics -- New Name of the Theory of Scientific Information”

Chemical informatics is …

More usually know as chemoinformatics or cheminformatics

Very differently defined, reflecting its cross-disciplinary nature Librarian Chemist (synthetic, medicinal, theoretical) Biologist / Bioinformatician Molecular modeler Pharmaceutical or Chemical Engineer Computer Scientist / Informatician

More definitions

Computational Chemistry – The application of mathematical and computational methods to particularly to theoretical chemistry

Molecular Modeling – Using 3D graphics and optimization techniques to help understand the nature and action of compounds and proteins

Computer-Aided Drug Design – The discipline of using computational techniques (including chemical informatics) to assist in the discovery and design of drugs.

Traditional areas of application

Pharmaceutical & life science industry particularly in early stage drug design

Databases of available chemicals Electronic publishing

including searchable chemical structure information in journals, etc.

Government and patent databases

The –ics so far (1960’s to present) … How do you represent 2D and 3D chemical structures?

Not just a pretty picture How do you search databases of chemical structures?

Google doesn’t help (much, but it might do soon…) How do you organize large amounts of chemical information? How do you visualize chemical structures & proteins? Can computers predict how chemicals are going to behave

… in the test tube? … in the body?

Current trends & hot topics

The decorporatization of chemical informatics (PubChem, MLI, eScience, open source)

Service-oriented architectures Packaging & processing large volumes of complex

information for human consumption Integration with other –ics (bioinformatics,

genomics, proteomics, systems biology)

Main players (Commercial)

MDL www.mdl.com Tripos, inc. www.tripos.com Accelrys www.accelrys.com Daylight CIS, inc. www.daylight.com

Main players (Academia)

“Pure” Chemoinformatics University of Sheffield, UK (Willett / Gillet)

http://www.shef.ac.uk/uni/academic/I-M/is/research/cirg.html Erlangen, Germany (Gasteiger)

http://www2.chemie.uni-erlangen.de/ Cambridge Unilever Center

http://www-ucc.ch.cam.ac.uk/ Indiana University School of Informatics

http://www.informatics.indiana.edu/ Related (computational chemistry, etc.)

UCSF (Kuntz) http://mdi.ucsf.edu/

University of Texas (Pearlman) http://www.utexas.edu/pharmacy/divisions/pharmaceutics/faculty/pearlman.html

Yale (Jorgensen) http://zarbi.chem.yale.edu/

University of Michigan (Crippen) http://www.umich.edu/~pharmacy/MedChem/faculty/crippen/

“Traditional” Journals

Journal of Chemical Information & Modeling (formerly JCICS) http://pubs.acs.org/journals/jcisd8/index.html

Journal of Computer-Aided Molecular Design http://www.kluweronline.com/issn/0920-654X

Journal of Molecular Graphics and Modeling http://www.elsevier.com/inca/publications/store/5/2/5/0/1/2/

Journal of Computational Chemistry http://www3.interscience.wiley.com/cgi-bin/jhome/33822

Journal of Chemical Theory and Computation http://pubs.acs.org/journals/jctcce/

Journal of Medicinal Chemistry http://pubs.acs.org/journals/jmcmar/

“Informal” publications

Network Science (online) http://www.netsci.org/Science/index.html

Chemical & Engineering News http://pubs.acs.org/cen/

Drug Discovery Today http://www.drugdiscoverytoday.com/

Scientific Computing World http://www.scientific-computing.com/

Bio-IT World http://www.bio-itworld.com/

CINF-L Distribution List

Chemical Information Sources Discussion List

Created by Gary Wiggins at IUB http://www.indiana.edu/~cheminfo/

network.html

Yahoo! Chemoinformatics Discussion List For

Job postings Ideas exchange Questions Industry – Student

connections All students encouraged to

join Open to others

To join, go to http://groups.yahoo.com/group/chemoinf

Or send an email to chemoinf-subscribe@yahoogroups.com

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/

Example 23D Visualization & Docking

3D Visualization of interactions between compounds and proteins

“Docking” compounds into proteins computationally

3D Visualization

X-ray crystallography and NMR Spectroscopy can reveal 3D structure of protein and bound compounds

Visualization of these “complexes” of proteins and potential drugs can help scientists understand the mechanism of action of the drug and to improve the design of a drug

Visualization uses computational “ball and stick” model of atoms and bonds, as well as surfaces

Stereoscopic visualization available

Docking algorithms

Require 3D atomic structure for protein, and 3D structure for compound (“ligand”)

May require initial rough positioning for the ligand Will use an optimization method to try and find the

best rotation and translation of the ligand in the protein, for optimal binding affinity

Genetic Algorithms

Create a “population” of possible solutions, encoded as “chromosomes”

Use “fitness function” to score solutions Good solutions are combined together

(“crossover”) and altered (“mutation”) to provide new solutions

The process repeats until the population “converges” on a solution

Traditional Workflow of Molecular Modeling

SupercomputerResearcherResearcherFORTRAN Code,

Scripts,Visualization Code

Hard DriveDirectory Jungle

Chemical Concepts

Experiments

Highly inefficient workflow (no automation)

Knowledge is human bound (grad student leaves and projects dies)

Incorporation with other DB’s is done in Researcher’s head

Varuna – a new environment for molecular modeling

QMDatabase

Supercomputer

ResearcherResearcher

Simulation ServiceFORTRAN Code,

Scripts

Chemical Concepts

Experiments

QM/MMDatabasePubChem, PDB,

NCI, etc.

Chem-GridChem-Grid

ReactionDB

DB ServiceQueries, Clustering,

Curation, etc.

Tools for mining the data

Tripos Benchware HTS Dataminer (formerly SAR Navigator), www.tripos.com