Big data is being reshaped thanks to 100-year-old ideas about geometry

This same guiding principle of using topological techniques on big data also has applications in drug development and other cutting-edge endeavours.Topology is a branch of modern geometry with roots going back to a foundational observation by the Swiss mathematician Leonhard Euler (1707-1783) about polyhedra, 3D shapes with flat faces, straight edges and sharp corners or “vertices”. The researchers who created the subject didn’t have real-world applications on their minds, they were just interested in what was mathematically true about shapes under certain conditions.Yet some of these ideas from topology that have been around for over 100 years are now finding significant applications in data science. So producing drugs with these shaped molecules enables them to target and affect the right cells.As it turns out, manufacturing a molecule to have a particular shape is a rather simple process. Having a map of this space would be a tremendous tool for producing new drugs, particularly if the map included landmarks indicating higher chances of solubility.In recent work, researchers used topological data analysis tools as a first step to producing such a map. This improved ability to produce water-soluble drugs has the potential to significantly shorten the time it takes to create a new treatment, and to make the whole process cheaper.In more and more realms of science, researchers are finding themselves with more data than they can effectively make sense of. The response of modern mathematicians to meet the mathematical challenges of big data is still unfolding – and topology, a theory bound only by the imagination of its practitioners, is bound to help shape the future.This article is republished from The Conversation by Ittay Weiss, Lecturer in Mathematics, University of Portsmouth under a Creative Commons license.