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Grid-enabled drug discovery to address neglected diseases
Dr. Marc Zimmermann
Department of Bioinformatics
Fraunhofer Institute for Algorithms and Scientific Computing (SCAI)
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Content
The challenges of the drug discovery
A pharmaceutical grid for the drug discovery
A pharmaceutical grid for a neglected disease
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Phases of a pharmaceutical development
Target discovery
Target
Identification
Target
Validation
Database
filtering
Similarity
analysis
vHTS
Lead discovery
Lead
Identification
Alignment
Biophores
Lead
Optimization
Clinical Phases (I-III)
QSAR
ADMET
diversity Combinatorial de novo
selection
libraries
design
Computer Aided
Drug Design
(CADD)
Duration: 12 – 15 years, Costs: 500 - 800 million US $
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Computational aspects of Drug Discovery : virtual screening
Enable scientists to quickly and easily find compounds binding to a particular target protein
- growth of targets number
- growth of 3D structures determination (PDB database)
- growth of computing power
- growth of prediction quality of protein-compound interactions
Experimental screening very expensive : not for academic or small companies
Actives molecules
Aim :
Enrichment =
Tested molecules
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Dataflow and workflow in a virtual screening
ligand data base
docking
MD-simulation
hit
Structure
optimization
Reranking
junk
crystal structure
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Content
The challenges of the drug discovery
A pharmaceutical grid for the drug discovery
A pharmaceutical grid for a neglected disease
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A shared in silico resource
To guarantee and preserve knowledge in the areas of discovery, development,
manufacturing, marketing and sales of next drug therapies
- Provides extremely large CPU power to perform computing intense tasks in a
transparent way by means of an automated job submission and distribution facility
- Provides transparent and secure access to storage and archiving of large
amounts of data in an automated and self-organized mode
- Connects, analyses and structure data and metadata in a transparent mode
according to pre-defined rules (science or business process based)
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Structure of a grid for drug discovery
Statistical models, optimization
Data and Knowledge Mining Services
Ontologies / Knowledge Representation
Workflows
Integration of Applications
Distributed Data Access / Information Retrieval
Construction in function of the
disease/subject of the grid
Virtual screening machine with
formal description
Meta-information on softwares
and formats
Semantic inconsistence between
biological and chemical databases
=> ontology-based mediation services
Administration of Virtual Organisations
Basic Grid Technology Layer
Users integration from different
and heterogeneous organisations
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Grid engine
Content
The challenges of the drug discovery
A pharmaceutical grid for the drug discovery
A pharmaceutical grid for a neglected disease
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Overview on neglected diseases
Infectious diseases kill 14 million people each year, more than 90% of whom are in the developing world.
Access to treatment is problematic
-
the medicines are unaffordable,
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some have become ineffective due to drug resistance,
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and others are not appropriately adapted to specific local conditions and constraints.
Neglected diseases represent grave personal tragedies and substantial health and economic burdens
even for the wealthiest nations.
Drug discovery and development targeted at infectious and parasitic diseases in poor countries has
virtually ground to a standstill, so that these diseases are neglected.
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Grids for rare diseases and diseases of the developing world
In silico drug discovery process
(EGEE, SwissBioGRID, …)
SCAI Fraunhofer
Clermont-Ferrand
Support to local centres
in plagued areas
(genomics research,
clinical trials and vector
control)
Swiss Biogrid consortium
Local research centres
In plagued areas
The grid impact :
•Computing and storage resources for genomics research and in silico
drug discovery
•cross-organizational collaboration space to progress research work
•Federation of patient databases for clinical trials and epidemiology in
developing countries
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Collaborative environment
We will support such processes as:
- search of new drug targets through post-genomics requiring data management and
computing
- providing a large public database with drug like molecules and found hits
- massive docking to search for new drugs requiring high performance computing and
data storage
- handling of experimental data requiring data storage and management
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Acknowledgement
IN2P3/CNRS
Fraunhofer SCAI
Biozentrum Basel
Nicolas Jacq
Marc Zimmermann
Michael Podvinec
Jean Salzeman
Kai Kumpf
Torsten Schwede
Vincent Breton
Martin Hofmann
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