Biological macromolecules such as proteins or RNA, at the atomic scale, can be seen as extremely complex mobile systems. The development of methods for modeling the structure and the motion of such systems is essential to better understand their physiochemical properties and biological functions. In recent years, many computer scientists in Robotics and Artificial Intelligence (AI) have made significant contributions to modeling biological systems. Research expertise in planning, search, learning, evolutionary computation, constraint programming, machine learning, data mining is being used to make great progress on molecular motion, structure prediction, and design.

This workshop will explore the many connections between robotics and molecular modeling and will feature keynote speakers who work in robotics, learning, and computational structural biology. Participation is encouraged through paper submission and poster presentations. We will focus on interdisciplinary approaches to predict molecular structures, to simulate their motions, and to analyze structure-dynamics-function relationships. For example, probabilistic search techniques, originally developed for robot motion planning, have been used to model protein structure and flexibility. Recent results have shown exciting promise at exploring high-dimensional and complex molecular motions. Also, search algorithms, optimization techniques, and geometry methods stemming from the AI and robotics community research have produced a large and recent body of literature.

Interaction between the sub-communities of robotics, AI, and molecular modeling will be promoted through the sharing views, methods, and findings. Biological topics will be well explained so that they can be well understood even by non-experts.