Pharmaceutical companies spend billions testing prospective drugs by conducting “wet lab” experiments that can take years to complete. But what if the same results could be obtained in a matter of minutes by running computer model simulations instead? A Silicon Valley startup says it has created a novel machine learning algorithm that does just that.
TwoXar (pronounced “two-czar”) was founded last year by two men both named Andrew Radin (more on that later). The Radins were interested in using advances in data science and large-scale computing to speed up the pace of drug discovery, which would give pharmaceutical companies better candidates for clinical studies.
“Our core IP [intellectual property], if you will, is this ability to take extremely diverse data sets and draw relationships between those data sets,” CEO Andrew A.Radin says. “We’re combining clinical data in combination with gene expression assays, protein interaction networks, drug protein binding databases, and physical attributes about the molecules themselves.”
Read the full article on HPCwire’s sister publication, Datanami, at http://www.datanami.com/2015/09/24/accelerating-drug-discovery-with-machine-learning-on-big-medical-data/