A number of current trends that are being adopted to reshape the field of high-performance computing exist, including multi-core systems, accelerators, and software frameworks for large-scale intrinsically parallel applications. These trends intersect with recent developments in computational chemistry to provide new capabilities for computer-aided drug discovery. Although this review focuses primarily on the application domains of molecular modeling and biomolecular simulation, these computing changes are relevant for other computationally intensive tasks, such as instrument data processing and chemoinformatics.

It has long been recognized that knowledge of the 3D structures of proteins has the potential to accelerate drug discovery, but recent developments in genome sequencing, robotics and bioinformatics have radically transformed the opportunities. Many new protein targets have been identified from genome analyses and studied by X-ray analysis or NMR spectroscopy. Structural biology has been instrumental in directing not only lead optimization and target identification, where it has well-established roles, but also lead discovery, now that high-throughput methods of structure determination can provide powerful approaches to screening. X-ray analysis and NMR have defined the 3D structures of many drug targets and their complexes with substrate analogues and other inhibitors. Has this really transformed the way we discover drugs?