Scientific research often involves examining structural relationships in molecules since scientists strongly believe in the causal relationship between structure and function. Traditionally, researchers have identified these patterns, or motifs, manually using biochemical expertise. However, with the massive influx of new biochemical data and the ability to gather data for very large molecules, there is great need for techniques that automatically and efficiently identify commonly occurring structural patterns in molecules. Previous automated substructure discovery approaches have each introduced variations of similar underlying techniques and have embedded domain knowledge. While doing so improves performance for the particular domain, this complicates extensibility to other domains. Also, they do not address scalability or noise, which is critical for certain structural domains like macromolecules. In this paper, we present MotifMiner, a general toolkit for automatically identifying common motifs in most any scientific molecular dataset. We describe both our application framework and services for identifying motifs, as well as demonstrate the flexibility of our system by analyzing several disparate domains, including protein, drug, and MD simulation datasets.

Numerical simulation is replacing experimentation as a means to gain insight into complex physical phenomena. Analyzing the data produced by such simulations is extremely challenging, given the enormous sizes of the datasets involved. In order to make efficient progress, analyzing such data must advance from current techniques that only visualize static images of the data, to novel techniques that can mine, track, and visualize the important features in the data. In this paper, we present our research on a unified framework that addresses this critical challenge in two science domains: computational fluid dynamics and molecular dynamics. We offer a systematic approach to detect the significant features in both domains, characterize and track them, and formulate hypotheses with regard to their complex evolution. Our framework includes two paradigms for feature mining, and the choice of one over the other, for a given application, can be determined based on local or global influence of relevant features in the data.

Biological macromolecules play a fundamental role in disease; therefore, they are of great interest to fields such as pharmacology and chemical genomics. Yet due to macromolecules' complexity, development of effective techniques for elucidating structure-function macromolecular relationships has been ill explored. Previous techniques have either focused on sequence analysis, which only approximates structure-function relationships, or on small coordinate datasets, which does not scale to large datasets or handle noise. We present a novel scalable approach to efficiently discover macromolecule substructures based on three-dimensional coordinate data, without domain-specific knowledge. The approach combines structure-based frequent pattern discovery with search space reduction and coordinate noise handling. We analyze computational performance compared to traditional approaches, validate that our approach can discover meaningful substructures in noisy macromolecule data by automated discovery of primary and secondary protein structures, and show that our technique is superior to sequence-based approaches at determining structural, and thus functional, similarity between proteins.