High performance-oriented computer aided drug design approaches in the exascale era

In 2023, the first exascale supercomputer was opened to the public in the US. With a demonstrated 1.1 exaflops of performance, Frontier represents an unprecedented breakthrough in high-performance computing (HPC). Currently, more (and more powerful) machines are being installed worldwide. Computer-aided drug design (CADD) is one of the fields of computational science that can greatly benefit from exascale computing for the benefit of the whole society. However, scaling CADD approaches to exploit exascale machines require new algorithmic and software solutions.

Here, the authors consider physics-based and machine learning (ML)-aided techniques for the design of small molecule binders capable of leveraging modern parallel computer architectures. Specifically, the authors focus on HPC-oriented large-scale applications from the past 3 years that were enabled by (pre)exascale supercomputers by running on up tothousands of accelerated nodes.

In the area of ML, exascale computers can enable the training of generative models with unprecedented predictive power to design novel ligands, provided large amounts of high-quality data are available. Exascale computers could also unlock the potential of accurate ML-aided physics-based methods to boost the success rate of structure-based drug design campaigns. Currently, however, methodological developments are still required to allow routine large-scale applications of such rigorous approaches.