In the last two decades, great progress has been made in molecular modeling through robotics-inspired computational treatments of biological molecules. Deep mechanistic analogies between articulated robots and biomolecules have allowed robotics researchers to bring forth methods originally developed to address the robot motion planning problem in robotics to address and elucidate the relationship between macromolecular structure, dynamics, and function in computational structural biology.

Tight coupling of approaches based on robot motion planning with computational physics and statistical mechanics have resulted in powerful methods capable of elucidating protein-ligand binding, order of secondary structure formation in protein folding, kinetic and thermodynamic properties of folding and equilibrium fluctuations in proteins and RNA, loop motions in proteins, small-scale and large-scale motions in multimodal proteins transitioning between different stable structures, and more.

The objective of this tutorial is to introduce the broad community of researchers and students at ACM BCB to robotics-inspired treatments and methodologies for modeling structures and motions in biomolecules. A comprehensive review of of the current state of the art, ranging from the probabilistic roadmap approach to tree-based approaches, will be accompanied with specific detailed highlights and software demonstrations of powerful and recent representative robotics-inspired methods for peptides, proteins, and RNA.