Discovering Interesting Molecular Substructures for Molecular Classification

Abstract

Given a set of molecular structure data preclassified into a number of classes, the molecular classification problem is concerned with the discovering of interesting structural patterns in the data so that "unseen" molecules not originally in the dataset can be accurately classified. To tackle the problem, interesting molecular substructures have to be discovered and this is done typically by first representing molecular structures in molecular graphs, and then, using graph-mining algorithms to discover frequently occurring subgraphs in them. These subgraphs are then used to characterize different classes for molecular classification. While such an approach can be very effective, it should be noted that a substructure that occurs frequently in one class may also does occur in another. The discovering of frequent subgraphs for molecular classification may, therefore, not always be the most effective. In this paper, we propose a novel technique called mining interesting substructures in molecular data for classification (MISMOC) that can discover interesting frequent subgraphs not just for the characterization of a molecular class but also for the distinguishing of it from the others. Using a test statistic, MISMOC screens each frequent subgraph to determine if they are interesting. For those that are interesting, their degrees of interestingness are determined using an information-theoretic measure. When classifying an unseen molecule, its structure is then matched against the interesting subgraphs in each class and a total interestingness measure for the unseen molecule to be classified into a particular class is determined, which is based on the interestingness of each matched subgraphs. The performance of MISMOC is evaluated using both artificial and real datasets, and the results show that it can be an effective approach for molecular classification.

Compact in‐memory representation of large graph databases for efficient mining of maximal frequent sub graphs

“…Frequent sub graph mining is useful in various application domains like chemo‐informatics, 1,11,20 Communication network analysis 2,21,22 and biological network analysis, 23 and social network analysis 24 . Besides the above factors, the way graph databases are represented in memory has a major impact on the efficiency and scalability of the algorithms.…”

Perspectives on Knowledge Discovery Algorithms Recently Introduced in Chemoinformatics: Rough Set Theory, Association Rule Mining, Emerging Patterns, and Formal Concept Analysis

“…FSG was again benchmarked on the PTE data set. MISMOC is an algorithm for Mining Interesting Substructures in MOlecular data for Classification 155 which can use any frequent subgraph miner, and it has been implemented using FSG. Its unique feature is that, when classifying an unknown molecule, it uses measures of interestingness of the matched frequent subgraphs in order to place the molecule into the appropriate class.…”