Applying machine learning to challenges in the pharmaceutical industry
MIT continues its efforts to transform the process of drug design and manufacturing with a new MIT-industry consortium, the Machine Learning for Pharmaceutical Discovery and Synthesis. The new consortium already includes eight industry partners, all major players in the pharmaceutical field, including Amgen, BASF, Bayer, Lilly, Novartis, Pfizer, Sunovion, and WuXi. A large number of these have a research presence in Cambridge or the surrounding areas, allowing for close cooperation and the creation of a center for artificial intelligence (AI) applications in pharmaceuticals.
The drug discovery process can often be exceedingly expensive and time-consuming, but machine learning offers tremendous opportunities to more efficiently access and understand vast amounts of chemical data — with great potential to improve both processes and outcomes. The consortium aims to break down the divide between machine learning research at MIT and drug discovery research — bringing MIT researchers and industry together to identify and address the most significant problems.
As part of the broader initiative to bring together machine learning and drug research, in April, MIT hosted a summit led by Regina Barzilay, the Delta Electronics Professor of Computer Science, and Dina Katabi, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science. The summit gathered MIT researchers with leaders of technology, biotech, and regulatory agencies to engage in ways digital technologies and artificial intelligence can help address major challenges in the biomedical and health care industries. Read More