Solid-state nanopores provide a promising platform for label-free molecular sensing, but interpretation and optimisation of their electrical signals require molecular-level understanding of ion transport, protein–surface interactions and analyte translocation. This project continues NAISS 2025/22-1108, in which we established a molecular-dynamics framework for protein translocation in solid-state nanopores. The original project focused on C-reactive protein and a conventional nanopore geometry. During the allocation period, progress in our experimental programme led to the development of a scalable planar solid-state nanopore architecture with an approximately square cross-section and independently controllable length, width and height. We have therefore developed a new molecular model reflecting this experimentally realised planar geometry. During the next 12 months, we will use GPU-accelerated molecular dynamics to investigate protein translocation through planar solid-state nanopores, with particular emphasis on protein hormones relevant to women’s reproductive health, including luteinising hormone (LH), follicle-stimulating hormone (FSH) and human chorionic gonadotropin (hCG). We will investigate protein orientation and dynamics, protein–pore interactions, ion distributions, ionic current modulation and translocation behaviour under different nanopore geometries, voltages and electrolyte conditions. Multiple independent simulations will be required to sample orientation- and trajectory-dependent behaviour. Open-pore simulations will be connected to experimental measurements from the planar nanopore platform, allowing the molecular model to be assessed against measured ionic-transport behaviour. The validated simulations will then be used predictively to explore how nanopore dimensions and operating conditions influence protein-dependent current signals and to identify promising designs for subsequent fabrication and experimental testing. Production simulations will use NAMD on the NVIDIA GH200 GPUs of Arrhenius. CPU resources will be used for system preparation, trajectory processing and analysis. The expected outcome is an experimentally anchored computational–experimental workflow for understanding and optimising planar solid-state nanopores for protein sensing.