When Zebiai Therapeutics Inc.’s CEO, Rick Wagner, went about naming his new machine learning company, he wanted it to connote something dramatic that displayed the company’s potential to reach into the seemingly boundless future technology had unlocked.
The next wave of drug discovery is being enabled by artificial intelligence (AI). This fact has not been lost on investors, who are keeping a close watch on emerging biopharma companies that are using AI and machine learning to enable the discovery of next-generation medicines.
For biopharma, 2019 can be described as a terrific year – with a few asterisks. The financial markets were flourishing, with venture capital dollars, in particular, flowing to the sector, while dealmaking reached historic proportions. Meanwhile, scientific breakthroughs led the way as cell and gene therapies gained ground, the first signs of success emerged with new technologies like CRISPR and the long-awaited promise of genomics found its way to the front lines of health care.
Machine learning and artificial intelligence (AI) are already being actively used in drug discovery to evaluate potential binding of small-molecule drugs to proteins, but there's potential for the technologies to be used on the development side as well, especially in hard-to-treat diseases such as Alzheimer's disease.
Researchers from Johns Hopkins University School of Medicine have developed a machine learning program that could score the risk of pancreatic cysts and recommend one of three treatment strategies – surgery, watchful waiting or discharge without follow-up – more accurately than current methods. The program could potentially reduce the number of unnecessary surgeries performed on pancreatic cysts with little to no potential of turning cancerous.