In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of\ud methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the\ud past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially\ud render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to\ud discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no\ud reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic\ud partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated\ud load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer\ud Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed\ud approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable\ud for large-scale, multi-domain, heterogeneous environments, such as computational grids
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