340038 Integrating Multiple Molecular Model Types With Stochastic Modeling

Tuesday, November 5, 2013: 3:35 PM
Union Square 11 (Hilton)
Andrew D. White and Shaoyi Jiang, Chemical Engineering, University of Washington, Seattle, WA

Molecular modeling has become a sprawling field, with models spanning many length and time-scales, being phenomenological or physics-based, and being biased or unbiased. In machine-learning, it has been observed that integrating multiple models often leads to results better than any one model. In molecular modeling, there is little progress on this topic despite the plethora of model types and simulation techniques. In this presentation, I'll discuss how to integrate multiple models and data types within the framework of graphical models, a type of stochastic modeling. Through the use of graphical models, we've developed integrative models which integrate QSAR models, sequence motif models, and simulation results. These models are capable of excellent antimicrobial peptide activity prediction while retaining model parameters that are easy to interpret. An example graphical model is shown in Figure 1. These integrative models are dynamic Bayesian networks and can encode multiple data types and constraints. Despite their complexity, dynamic Bayesian networks may be quickly built and trained due to the generality of graphical model algorithms. I'll also discuss preliminary results on combining simulation with experiments and other simulations.

Figure 1: A graphical model which classifies sequences based on their motifs and amino acid distributions. The classifier was trained on antimicrobial peptides and performed with over 90% accuracy on predicting the activity of unobserved peptides.

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