Optimizing Low Energy Pathways in ReceptorLigand Binding with Motion Planning Determination of ligand binding pathways is an important factor to predict drug efficacy in drug discovery. Ligandreceptor binding involves the motion of many degrees of freedom, which can make binding pathways difficult to discover with traditional methods. Interactive molecular docking tools can allow users to explore the high dimensional energy landscape of the ligandreceptor system with rigid molecular models to determine low energy ligand states and pathways to binding. To introduce the effect of ligand flexibility in molecular docking with rigid body models, we use ensembles of distinct ligand conformation states that can be swapped during exploration. Our method emulates ligand flexibility effects in rigid body docking at no extra computational cost. Our automated method simulates user search performance with a path optimization algorithm. We find that allowing the algorithm to include different ligand conformations in its search for states of lower energy can result in optimized low energy pathways with reduced search times in difficult areas near energy barriers. This method can be adapted to include molecular flexibility effects in interactive rigid body molecular docking running in commodity hardware, such as molecular docking games.

Rosetta LigandProtein Docking with SelfAdaptive Differential Evolution Molecular Docking is a growing area of research due to its importance in structuralbased drug design. Along the years, many computational methods have been proposed as a possible solution for the molecular docking problem, but none of them genuinely solved it. In computational theory, this problem is treated as an NPHard one, meaning that there are no efficient algorithms to find the best solution in polynomial time. In this way, metaheuristics became an option for finding feasible solutions in a viable computational time. Despite that, metaheuristics are parameterdependent methods which must be tuned to find the balance between exploitation and exploration capabilities. In this sense, we used a SelfAdaptive Differential Evolution algorithm with four different mutation techniques in the flexibleligand rigidreceptor molecular docking problem and thoroughly compared them with the single mutation versions  something not yet explored. Also, we used the Rosetta energy function as a score function for binding evaluation, which is not broadly used in most of the related works. Our results showed that SelfAdaptive Differential Evolution could reach competitive results in comparison with different algorithms that used the same energy function and molecular flexibility.