chemical informatics & cyberinfrastructure collaboratory hts data analysis & virtual...
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Chemical Informatics & Cyberinfrastructure Collaboratory HTS Data Analysis & Virtual Screening . David J. Wild Visiting Assistant Professor Indiana University School of Informatics [email protected] http://www.informatics.indiana.edu/djwild/. Content. - PowerPoint PPT PresentationTRANSCRIPT
David Wild – ECCR Meeting, October 2005. Page 1 Indiana University School of
Chemical Informatics & Cyberinfrastructure Collaboratory
HTS Data Analysis & Virtual Screening
David J. Wild
Visiting Assistant ProfessorIndiana University School of Informatics
[email protected]://www.informatics.indiana.edu/djwild/
David Wild – ECCR Meeting, October 2005. Page 2 Indiana University School of
Content• Web services framework for HTS data analysis
– Long-term approach
• Priorities for web service development– Rapid dataset organization using cluster analysis– Interface tools for navigation and analysis– Virtual screening
David Wild – ECCR Meeting, October 2005. Page 3 Indiana University School of
Thoughts relating to Pubchem HTS analysis
(and more widely applicable)• Existing approaches do not scale up well• Scientists’ questions are probably not going to be conceptually
complex, but finding the answers can currently be very time consuming and/or complex (for a human)– “who else is working on this chemical structure I just made (or
similar ones)?”– “are there any compounds in Pubchem (or elsewhere) that might
bind to the active site of this protein I just resolved?”– “do any compounds related to this one exhibit toxic side effects?”
• We need to figure out just what the questions are!(Contextual Inquiry, Use cases)
• 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
David Wild – ECCR Meeting, October 2005. Page 4 Indiana University School of
Purpose ToolsInteraction Layer Software for information
access and storage by humans, including email, browsing tools and “push” tools
Web browsers, email clients, RSS aggregators, JMol, JME
Aggregation Layer
Software, intelligent agents and data schemas customized for particular domains, applications and users
BPEL, Microsoft Smart Client
Interface Layer Common interfaces to the data layer – may be several for different kinds of information
Apache web services, SOAP wrappers, WSDL, UDDI, XML, Microsoft .NET
Data Layer Comprehensive data provision including storage, calculation, semantics and meta-data, probably in multiple systems
MySQL, PostgreSQL, gNova Cartridge chemoinformatics calculation programs; data from NCI, ZINCWild, D.J., Strategies for Using Information Effectively in Early-stage Drug
Discovery, in Ekins, S. (ed), Computer Applications in Pharmaceutical Research and Development, submitted July 2005
David Wild – ECCR Meeting, October 2005. Page 5 Indiana University School of
Purpose ToolsInteraction Layer Software for information
access and storage by humans, including email, browsing tools and “push” tools
Web browsers, email clients, RSS aggregators, JMol, JME
Aggregation Layer
Software, intelligent agents and data schemas customized for particular domains, applications and users
BEPL, Microsoft Smart Client
Interface Layer Common interfaces to the data layer – may be several for different kinds of information
Apache web services, SOAP wrappers, WSDL, UDDI, XML, Microsoft .NET
Data Layer Comprehensive data provision including storage, calculation, semantics and meta-data, probably in multiple systems
MySQL, PostgreSQL, gNova Cartridge chemoinformatics calculation programs; data from NCI, ZINCWild, D.J., Strategies for Using Information Effectively in Early-stage Drug
Discovery, in Ekins, S. (ed), Computer Applications in Pharmaceutical Research and Development, submitted July 2005
web servicesdatabases & tools
intelligent agentshuman interfaces
David Wild – ECCR Meeting, October 2005. Page 6 Indiana University School of
Onlinedatabase
(e.g. PubChem)
Localdatabase
3D DockingTool
2D-3Dconverter
3Dvisualizer
UDDI
New Structure ServiceSearch online databases
for 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
“find me all thestructures that fit theenclosed protein forThe next three months”
David Wild – ECCR Meeting, October 2005. Page 7 Indiana University School of
Priorities for web service development
• Rapid dataset search and organization– Search of PubChem (SOAP interface already exists)– Search of local gNova / PostgreSQL database– Clustering using BCI (Digital Chemistry) Divisive K-Means– BCI Markush searching
• Interface tools for navigation and analysis– Integration with Spotfire– ChemTK (or other spreadsheet-metaphor product)– Develop entirely new interface tools (usability studies)
• Virtual Screening– Molecular docking with OpenEye FRED– Property calculation with Molinspiration / Chemaxon– PDB Search (EMBL)– Activity prediction modules (Molinspiration / RP / SVMs etc)
David Wild – ECCR Meeting, October 2005. Page 8 Indiana University School of
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, and deserve investigation for future use in this project
• 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
David Wild – ECCR Meeting, October 2005. Page 9 Indiana University School of
Visualization methods for datasets &
clusters• Partitions– Spreadsheets– Enhanced Spreadsheets– 2D or 3D plots
• Hierarchies– Dendograms– Tree Maps– Hyperbolic Maps
David Wild – ECCR Meeting, October 2005. Page 10 Indiana University School of
Supplemental Slides
David Wild – ECCR Meeting, October 2005. Page 11 Indiana University School of
David Wild – ECCR Meeting, October 2005. Page 12 Indiana University School of
David Wild – ECCR Meeting, October 2005. Page 13 Indiana University School of
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
David Wild – ECCR Meeting, October 2005. Page 14 Indiana University School of
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
David Wild – ECCR Meeting, October 2005. Page 15 Indiana University School of
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
David Wild – ECCR Meeting, October 2005. Page 16 Indiana University School of
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
David Wild – ECCR Meeting, October 2005. Page 17 Indiana University School of
Use Case - PubchemWhich 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
David Wild – ECCR Meeting, October 2005. Page 18 Indiana University School of
Use Case – PubChemWhich 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 BCI cluster analysis– Structure-activity relationships lots of methods– Chemical and pharmacokinetic properties mitools,
chemaxon– Compound history gNova / PostgreSQL / Pubchem search– Patentability BCI Markush handling software– Toxicity– Synthetic feasibility– + requires visualization tools!
David Wild – ECCR Meeting, October 2005. Page 19 Indiana University School of
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
David Wild – ECCR Meeting, October 2005. Page 20 Indiana University School of
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
David Wild – ECCR Meeting, October 2005. Page 21 Indiana University School of
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)
David Wild – ECCR Meeting, October 2005. Page 22 Indiana University School of
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
David Wild – ECCR Meeting, October 2005. Page 23 Indiana University School of
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
David Wild – ECCR Meeting, October 2005. Page 24 Indiana University School of
Comparative execution timesNCI subsets, 2.2 GHz Intel Celeron processor
7h 27m
3h 06m
2h 25m
44m0
5000
10000
15000
20000
25000
30000
0 20000 40000 60000 80000 100000 120000Number of Structures in Clustered Set
Exe
cutio
n Ti
me
(s)
Wards
K-means
Divisive K-means
Parallel Divisive Kmeans (4-node)
David Wild – ECCR Meeting, October 2005. Page 25 Indiana University School of
Clustering a 1 million compound dataset
on a 2.2 GHz Celeron Desktop MachineMethod 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….
David Wild – ECCR Meeting, October 2005. Page 26 Indiana University School of
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)
David Wild – ECCR Meeting, October 2005. Page 27 Indiana University School of
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
David Wild – ECCR Meeting, October 2005. Page 28 Indiana University School of
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
David Wild – ECCR Meeting, October 2005. Page 29 Indiana University School of
Visualization methods for datasets &
clusters• Partitions– Spreadsheets– Enhanced Spreadsheets– 2D or 3D plots
• Hierarchies– Dendograms– Tree Maps– Hyperbolic Maps
David Wild – ECCR Meeting, October 2005. Page 30 Indiana University School of
David Wild – ECCR Meeting, October 2005. Page 31 Indiana University School of
David Wild – ECCR Meeting, October 2005. Page 32 Indiana University School of
VisualiSAR – with a nod to Edward Tufte.See http://www.daylight.com/meetings/mug99/Wild/Mug99.html
David Wild – ECCR Meeting, October 2005. Page 33 Indiana University School of
Tree Maps – very Tufte-esque
David Wild – ECCR Meeting, October 2005. Page 34 Indiana University School of
External support• ECCR grant ($500,000)
– 20% Co-PI with Fox for development of web services for HTS data organization and visualization
– May lead to $5m/5 years grant for full center• Applied for Microsoft Smart Clients for eScience grant
($50,000)– Including Marlon Pierce in the Community Grids lab
• Peter Murray-Rust group, Cambridge – offering expertise and assistance with web services
• IO-Informatics – provision of Sentient software and consulting• BCI – clustering, structure enumeration & toolkit, consulting• OpenEye – a range of calculation tools, FRED docking• Molinspiration – MiTools Toolkit• gNova – CHORD chemical database system• Possible financial support from company in the UK
David Wild – ECCR Meeting, October 2005. Page 35 Indiana University School of
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/specific
ation/ws-bpel/• Microsoft .NET & C#
David Wild – ECCR Meeting, October 2005. Page 36 Indiana University School of
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)
David Wild – ECCR Meeting, October 2005. Page 37 Indiana University School of
Sentient - an alternative approachto managing heterogenous data
sources• Collaboration with IO-Informatics (along with Cornell, and UCSD) for the investigation of service-oriented architectures in life sciences research using Sentient software
• Aim to integrate several sources of information relating to Alzheimer’s Disease (brain imaging, morphology, gene expression) so that cross-dataset biomarkers can be identified
• Sentient usies Intelligent Multidimensional Objects (IMOs) to define and query data sources and the tools used toaccess them
• Still a browsing approach, but with a layer of coherenceand “intelligence”
• Hope to expand to include chemistry data• Can also be used as an interface-level tool
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David Wild – ECCR Meeting, October 2005. Page 39 Indiana University School of
David Wild – ECCR Meeting, October 2005. Page 40 Indiana University School of
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!)