HCLS Dev Summit: AI for Biology - Protein Structure Prediction and Beyond
, Senior Developer Relations Manager, NVIDIA
Originally developed to understand human language, self-supervised natural language processing models have recently been instrumental in understanding and predicting the structure and function of biomolecules like proteins. Much like they do for natural language, transformer-based representations of protein sequences provide powerful embeddings for use in downstream AI tasks, like predicting the final folded state of a protein, understanding the strength of protein-protein or protein-small molecule interactions, or in the design of protein structure provided a biological target. Here, we review some of the most exciting recent advancements and model architectures as a window into the tools, techniques, and infrastructure required to advance the field.