High-Performance, High-Throughput Approaches to Computational Chemistry
Thursday, December 12, 2013 - 2:00pm - 2:50pm
Recent Nobel prizes highlight the contribution of computation in the field of biochemistry. The Big Data era approaches computational modeling by integrating classical physics and chemistry with data-driven computation. Using knowledge derived from the electrostatic models of Warshel and Levitt, or the dynamic simulations of Karplus, patterns in data lead to predictive models of molecular function and mechanism. We show how massive computation can enable costly computations across large protein data sets. Model optimization also shows dramatic speedup with the use of high-throughput machine learning. By exhaustive sampling of the model space, we explore its “shape” and pursue better methods for feature selection and model robustness.