Kyle Beauchamp's PyData NYC 2014 talk: Engineering a full Python stack for biophysical computation

Postdoc Kyle Beaucamp gave a talk about our Python stack for biophysical computation at PyData NYC 2014.  The whole talk is now available online:

Youtube link:

Slides for the talk can be found here:

Engineering new therapeutics is hard--and getting harder. Accurate physical modeling promises to improve the way we design drugs, but the necessary open source infrastructure is lacking. The Omnia Consortium---a collaboration of multiple academic laboratories working on physical modeling tools for drug discovery---is producing a suite of open-source tools for understanding drugs, proteins, and the biomolecular mechanisms of disease. Our Python-centric software stack is uses Python, Cython, C++, and CUDA/OpenCL to achieve bleeding-edge performance. Part of our stack (OpenMM) is also implemented on the Folding@Home distributed computing project and currently runs on tens of thousands of high-end GPUs around the world, producing over 18PFLOP/s of computational power. In our talk, we will introduce biophysical simulation and its application to understanding mechanisms of disease and its potential for designing new therapeutics. We will discuss the challenges in building robust tools for automating and scaling up biophysical simulations, compared with the relatively mature tools already available for modern data science. We will describe some of the tools in our stack (OpenMM, MDTraj, MSMBuilder, Yank) and how we use the conda packaging environment to facilitate distribution of our domain-specific code. Finally, we will discuss our plans to improve physical models and study drug resistance using iterative cycles of modeling and automated biophysical experiments performed at Memorial Sloan-Kettering Cancer Center.