Computing Challenges for Structure-based Drug Design on a Genomic Scale
Tod M. Klingler, Structural GenomiX
At Structural GenomiX we are integrating experimental approaches for protein structure determination with computational modeling methods, including comparative modeling, ab initio prediction and molecular dynamics, to produce the most comprehensive and accurate view of protein structure space. Using this view of protein structure space as a starting point, large-scale structure-based drug design will be used to greatly improve the drug development process. Computational techniques for docking chemical structural to protein structures are the core of this platform. The required algorithms for protein modeling and chemical docking are compute-intensive and often require specific tuning. In this talk I will describe several of these computational approaches, their integration, and some of the automation and high-throughput computing challenges we are facing in developing this new platform for drug discovery drug.
National Digital Mammography Archive
Robert Hollebeek, University of Pennsylvania
The National Digital Mammography Archive is funded by the National Library of Medicine to design and implement a secure digital archive for mammography and associated reports using Next Generation Internet technologies, including high bandwidth optical networks, quality of service, scalable systems, and scalable applications. Images and reports will be rapidly available wherever needed for medical or educational purposes thus improving screening, diagnosis and ultimately, patient care. Researchers from the Universities of Pennsylvania, Chicago, North Carolina and Toronto, team with advanced computing groups from the University of Pennsylvania (NSCP) and BWXY (Oak Ridge ACT), to develop integrated systems for high-speed networking, distributed archiving, and secure applications. The talk will demonstrate how images and patient data can be securely moved to and from hospitals to an archive and how the applications, including computer assisted diagnosis (CAD), data mining, and teacher training collections, could be used for clinical and research purposes.