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In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute's HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a nondedicated computational environment. These features make it suitable for large-scale, multidomain, heterogeneous environments, such as computational grids
biology computing, data mining, distributed algorithms, molecular biophysics, molecular configurations, pattern recognition, peer-to-peer computing, resource allocation, tree searching,dynamic load balancing, distributed algorithm, frequent subgraph mining, interesting pattern discovery, irregular search tree, peer-to-peer communication framework, molecular structure distributed mining, molecular biology, search space dynamic partitioning, receiver-initiated load balancing algorithm,Load management, Drugs, Data mining, Computational complexity, Distributed algorithms, Peer to peer computing, Partitioning algorithms, Cancer, Workstations, Large-scale systems,Distributed computing, peer-to-peer computing, dynamic load balancing, subgraph mining, frequent patterns, biochemical databases, molecular compounds.
"Dynamic Load Balancing for the Distributed Mining of Molecular Structures", IEEE Transactions on Parallel & Distributed Systems, vol. 17, no. , pp. 773-785, August 2006, doi:10.1109/TPDS.2006.101
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