  In drug discovery, molecular docking is the task in charge of estimating the
position of a molecule when interacting with the docking site. This task is
usually used to perform screening of a large library of molecules, in the early
phase of the process. Given the amount of candidate molecules and the
complexity of the application, this task is usually performed using
High-Performance Computing (HPC) platforms. In modern HPC systems,
heterogeneous platforms provide a better throughput with respect to homogeneous
platforms. In this work, we ported and optimized a molecular docking
application to a heterogeneous system, with one or more GPU accelerators,
leveraging a hybrid OpenMP and OpenACC approach. We prove that our approach has
a better exploitation of the node compared to pure CPU/GPU data splitting
approaches, reaching a throughput improvement up to 36% while considering the
same computing node.
