What’s happening at Polaris.

While fields like natural language processing (NLP) and computer vision (CV) have long benefitted from standardized benchmarks and well-organized competitions, ML for drug discovery (MLDD) faces unique challenges. What can we learn from other fields and what does the current landscape of competitions look like in MLDD?

With the Polaris Hub, we set out to design a universal data format for ML scientists in drug discovery. Whether you’re working with phenomics, small molecules, or protein structures, you shouldn’t have to spend time learning about domain-specific file formats, APIs, and software tools to be able to run some ML experiments. Learn more about how we support extra-large datasets, like BELKA, in this behind-the-scenes post!

Over the past few years, the field of machine learning for drug discovery (MLDD) has experienced an unprecedented surge of innovation. But how do we know whether these innovations can truly impact the development of new drugs? It's hard to tell.