As a leading mind in the field of computational biology and a pioneer of CMU's program on the topic, Murphy himself has played a strong role in this. In 2011, he penned a commentary noting that machine learning would play a role of growing importance in the drug discovery process. But his argument went a step further, advocating for the use of active machine learning, or a subset of ML in which the user offers the machine feedback on desired outcomes, improving its efficiency and accuracy over time. In the drug discovery process, the number of experiments required to screen a specific compound on a specific target while monitoring impact on other targets can quickly become unwieldy. Active ML offers researchers the opportunity to direct the experiment, supervising the computer as it iteratively chooses experiments that are most likely to improve the model.

"Machine learning can help, but you still need a strong framework from which the computer can quickly analyze data to help in the creation of safe and …

Robots are attractive to pharmaceutical companies because they're "relentless…they never stop," says Peter Harris, chief executive of HighRes Biosolutions, a Beverly, Mass.,-based company that supplies automated systems for pharmaceutical clients. The software that controls the machines "is able to keep track of many more things in parallel than a human." Lilly recently put $90 million into a new 300,000-square-foot research center in San Diego, where robots are helping to speed up the pace of scientific discovery. In one installation, four glass-enclosed robotic arms grow cells, isolate DNA, and place samples into roughly postcard-sized plastic "plates" that resemble miniature muffin trays. They also shuttle these between various equipment, like measuring machines and incubators, said Dan Skovronsky, president of Lilly's research labs.

Scientists working in tandem with artificial intelligence (AI) could slash the time it takes to develop new drugs - and, crucially, the cost - say tech companies. Developing pharmaceutical drugs is a very expensive and time-consuming business. And as AstraZeneca found out last week, disappointing drug trials can knock millions off your stock market value in a flash. So the faster we can identify promising molecules that could be turned into viable drugs, the better. This is why pharmaceutical companies, such as GlaxoSmithKline (GSK), Merck, Sanofi and Johnson & Johnson, are now turning to artificial intelligence (AI) to help them.