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O. Burchan Bayazit, Guang Song, Nancy M. Amato, "Ligand Binding with OBPRM and Haptic User Input: Enhancing Automatic Motion Planning with Virtual Touch," Technical Report, TR00-025, Department of Computer Science and Engineering, Texas A&M University, Oct 2000.
Technical Report(ps, pdf, abstract)

In this paper, we present a framework for studying ligand binding which is based on techniques recently developed in the robotics motion planning community. We are especially interested in locating binding sites on the protein for a ligand molecule. Our work investigates the performance of a fully automated motion planner, as well improvements obtained when supplementary user input is collected using a haptic device. Our results applying an obstacle-based probabilistic roadmap motion planning algorithm (obprm) to some known protein-ligand pairs are very encouraging. In particular, we were able to automatically generate con gurations close to, and correctly identify, the true binding site in the three protein-ligand complexes we tested. We nd that user input helps the planner, and a haptic device helps the user to understand the protein structure by enabling them to feel the forces which are hard to visualize.