Docking@Home Project Posters and Papers


Poster on the 2013 Supercomputing, Denvor, CO, November, 2013
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On Efficiently Capturing Scientific Properties in Distributed Big Data without Moving the Data: A case study in distributed structural biology using MapReduce

In this poster, we present two variations of a general analysis algorithm for large datasets residing in distributed memory systems. Both variations avoid the need to move data among nodes because they extract relevant data properties locally and concurrently and transform the analysis problem (e.g., clustering or classification) into a search for property aggregates. We test the two variations using the SDSC’s supercomputer Gordon, the MapReduce-MPI library, and a structural biology dataset of 100 million protein-ligand records. We evaluate both variations for their sensitivity to data distribution and load imbalance. Our observations indicate that the first variation is sensitive to data content and distribution while the second variation is not. Moreover, the second variation can self-heal load imbalance and it outperforms the first in all the fifteen cases considered.

Poster on the 2012 CBCB Research Symposium on Bioinformatics & Systems Biology, Newark, DE, May 2012
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A Scalable and Accurate Method for Classifying Protein-Ligand Binding Geometries using MapReduce

We present a scalable and accurate, 3-step, algorithm for classifying protein-ligand binding geometries in molecular docking. We analyze results for docking, cross-docking and ensemble docking for a series of HIV protease inhibitors. This algorithm demonstrates significant improvement over /“energy-based/” scoring for the accurate identification of near-native ligand structures. The advantages of our approach make it attractive for applications in real-world drug design

Poster on the 2012 Grace Hopper Celebration, Baltimore, MD, October 2012
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Benchmarking gender differences in Volunteer Computing

We conducted an open survey from October 2011 to July 2012 directed to the D@H volunteers. The primary objective of this survey is benchmarking current D@H volunteers to identify their demographics, preferences, and opinions with respect to our project. The final goal, however, is to obtain data that can inform the recruitment process in accordance with research-based practices for attracting diverse populations to computing and science.

Papers in conferences and journals











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