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MoleculaRnetworks: an integrated graph theoretic and data mining tool to explore solvent organization in molecular
Barbara Logan Mooney1, L René Corrales, Aurora E Clark
1Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ 87521, USA. barbaram@email.arizona.edu
Journal of Computational Chemistry
|January 27, 2012
Summary
New R scripts, moleculaRnetworks, analyze molecular simulation data for geometric and solvent network properties. Novel graph theory and PageRank-based algorithms reveal solute solvent environments and H-bonding patterns.
Area of Science:
- Computational chemistry
- Statistical computing
- Data mining
Background:
- Molecular simulations generate vast amounts of data on solute-solvent interactions.
- Analyzing this data requires specialized tools for structural and dynamical insights.
Purpose of the Study:
- To introduce moleculaRnetworks, a set of R scripts for processing and analyzing molecular simulation data.
- To provide novel algorithms for geometric and solvent network analysis of aqueous solutes.
Main Methods:
- Development of R scripts (moleculaRnetworks) for molecular simulation data processing.
- Application of graph theory and PageRank algorithm for solvent environment and H-bonding analysis.
- Implementation of a preprocessor for trajectory data and an interactive analysis script.
Main Results:
- Novel methods for identifying solvent geometric shape around solutes.
- Exploratory approach for H-bonding description using PageRank.
- Scripts enable statistical, trend, and correlation analysis of simulation data.
Conclusions:
- moleculaRnetworks enhances access to structural and dynamical information from molecular simulations.
- The scripts offer powerful tools for geometric and solvent network analysis.
- The methods are extensible to other hydrogen-bonded solvents.
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