Computer-aided drug design uses chemistry simulations and calculations to accelerate the discovery of drug candidate molecules. Computational resources and time are cheap relative to the painstaking research in experimental laboratories. Recent advances in computing technologies and computational chemistry have greatly improved the accuracy and scope of computer-aided drug design, furthering the scientific appetite for computational power. Many scientific computing software tools are based on hand-crafted legacy code, written in Fortran for example, and have not been modernized for use on parallel architectures. The greatest challenge to improving software performance is that most developers and users of these tools are trained in the physical sciences with little formal background in software design. Software development tools must be easy to use for this user base.
We describe our efforts and successes using the Intel® oneAPI Math Kernel Library and Threading Building Blocks to improve the performance of computational drug design software. We describe the application of these tools by the Open Source COVID-19 drug discovery consortium and partnering scientists.