Using motion planning to rank ligand binding affinity

In the drug discovery process, pharmaceutical companies have to screen many drug (or ligand) candidates to find the most promising ones for trial. This process is very costly and attention as turned to computational approaches to predict binding affinity to the desired target protein. In this work, we develop a computational tool for ranking ligand binding affinity that uniformly samples ligand conformations over the target protein's surface and analyzes the resulting set to compute an affinity ranking. Experiments on one tar-get protein shows that our method is able to correctly rank different ligands for the target protein as determined by ex-perimental data. Our method is a promising technique and potential cost-saving tool for pharmaceutical companies to narrow the search for good drug candidate. Copyright is held by the author/owner(s).