Artificial intelligence (AI) methods are poised to play an important role in understanding complex biological phenomena, including protein folding, protein-ligand interactions and self-assembly of peptides/ biological macromolecules. AI methods are now being regularly coupled to traditional high performance computing (HPC) workflows such as multi scale molecular simulations at unprecedented scales (e.g., recently the prestigious ACM/IEEE Gordon Bell award for use of HPC resources for COVID-19 research was awarded to group that used machine learning techniques coupled to molecular dynamics simulations of the SARS-CoV-2 Spike protein). While the convergence of AI and HPC is leading to access unprecedented scales for modeling and understanding complex biological phenomena, they also impose number of challenges -- from addressing the scale of biological systems that can be investigated, issues related to understanding and interpreting how AI methods analyze high dimensional datasets, and in general, how AI infrastructure can be better utilized by the broader community to benefit fundamental and applied biological research. We propose to bring together a community of researchers at the forefront of this technology to discuss and collaborate towards open solutions for these outstanding challenges.

This year, given the immediate relevance of COVID-19 research, we believe that a targeted meeting focused on how AI methods are accelerating discoveries in the mechanisms of SARS-COV-2 infections and aiding the design of small-molecule inhibitors and biologics targeting the virus would be of immensely beneficial. There has been an unprecedented global collaboration on how AI methods are enabling fundamental discoveries in designing effective therapeutics for COVID-19. We believe that bringing together this community of researchers involved in various aspects of COVID-19 research could be beneficial to share their research experience and how AI methods contributed to their research. In addition, this will also serve as a forum to exchange/ share knowledge on how remote collaborations and knowledge sharing across the world was enabled at such a short time. This model could serve as a future example for how collaborations at the time of need should proceed and open scientific dialogue and discussions could keep the community motivated, despite personal/social/political/economic distractions.