In the last two decades, many computer scientists in Artificial Intelligence (AI) and Robotics have made significant contributions to modeling biological systems. Indeed, the fields of computational structural biology are now highly populated by researchers with diverse background in search, planning, learning, evolutionary computation, constraint programming, machine learning, data mining, etc., and great progress is being made on methods to solve problems related to structure prediction, motion simulation, and design of biological macromolecules (proteins, RNA). These problems pose difficult search and optimization tasks on vast, high-dimensional, continuous search spaces underlined by non-linear multimodal energy surfaces.
An example of such interdisciplinary approaches is the application of probabilistic search techniques, originally developed for robot motion planning, to model protein structure and flexibility. Many search algorithms have been brought forth from this Robotics-based community, with a rich body of recent literature. On the other hand, another community within AI focuses on optimization issues with non-linear multimodal energy functions, and many researchers in this community propose evolutionary search frameworks for modeling structures and motions of proteins. Yet others focus on geometry, symmetry, discrete search, machine learning, and other frameworks to model biomolecular structures. Methods based on AI algorithms have also been proposed for computational protein design.
We believe the aforementioned sub-communities in AI and Robotics have now gained enough maturity and expertise in computational structural biology. It is important to provide venues for these researchers to come together and share views, treatments, and findings on protein and nucleic acid modeling. We hope this workshop will promote interactions for crosspollination of ideas, lead towards more powerful treatments, and in turn allow the field to make further progress.