Comparison of Molecule Graph Representation with Similarity Consistency Many research tasks in the bioinformatics area, such as bioactivity prediction, de novo molecular design, and synthesis prediction, increasingly utilize graphsimilaritybased analysis, which relies on selecting graph representation methods. However, most studies focus on the improvement of the final results of the tasks, and less attention has been paid on the differences of the distribution of the result of graph representation, (e.g. the embedded vectors) and how to evaluate them. In this paper, we propose a metric, mean degree of consistency (MDC), to evaluate the different approaches of graph representation with the help of retrieval results. To implement MDC, we introduce a serialized matching matrix and an optimization method based on partial sequence matching. We evaluate the efficiency and reliability of MDC with a series of experiments on graphsimilaritybased retrievals for molecule graphs (MGs). Our result is helpful to the graph researchers for selecting graph representation methods.

Accelerating LargeScale Molecular Similarity Search through Exploiting High Performance Computing Molecular similarity search is a simple but powerful chemoinformatics tool to rapidly find molecules that are structurally similar to a known reference compound from a large molecular database. A variety of indexing structures had been developed to improve the performance of similarity search over the large compound database. However, those algorithms often require a large computational cost to build indices and process queries, especially for a largescale molecular dataset. We study the problem of accelerating similarity search using high performance computing (HPC) and design general algorithms to speed up existing indexing algorithms. We first propose a parallel algorithm based on data chunking, working for all indexing algorithms for similarity search. We theoretically analyze its computation cost and relationships between the speedup and number of data chunks. We further propose a parallel query algorithm for all graphbased indexing algorithms to accelerate their query processing in HPC. Both of our algorithms consistently offer a greater speedup than the baseline algorithm(s) when evaluated with different datasets and parameter settings.