The emergence of Artificial Intelligence [AI] within the past few years has garnered either optimism, skepticism, or fear as we see an increase in adoption, from everyday smart products to large-scale innovation. Often acknowledged as a game-changing technology, AI offers untapped potential in improving established ways of doing business, as well as with new opportunities in meeting and addressing critical pain points across many industries, including banking, manufacturing and healthcare. The pharmaceutical industry is also embracing the trendy technology for its abilities in effectively advancing and/or addressing the ever-changing drug or therapeutic needs from those who suffer from everyday viruses to complex diseases, like pancreatic cancer or Alzheimer's. As we see new renditions of once-eradicated viruses or destructive diseases like polio, traditional R&D efforts can be ineffective and expensive, often taking between 11 - 15 years and with costs upwards of $2.6 billion. AI-powered drug discovery efforts are enabling big pharma and biotechnology companies to streamline R&D efforts, including calculating vast patient datasets into digestible, tangible information, identifying personalized / precision medicine opportunities or forecasting potential responses to new drugs.

Artificial intelligence (AI) is in the midst of a renaissance. New techniques are producing results, such as the defeat of a Go world champion by an AI system developed by a subsidiary of Google, which would have been unthinkable five years ago. These techniques are not just being used to play games. Today, AI is being applied to one of humanity's most daunting challenges: the hunt for a cure for cancer. AI has huge potential for helping scientists manage the mind-boggling complexities of research and other data, to expedite cancer drug discovery and translate scientific findings into real benefits for patients.

Ten years ago people thought they were crazy, but today the team behind Boston-based pharma start-up BERG is catching the industry's attention with its data-driven, back-to-biology approach to drug discovery. A combination of maths and biology, they believe – and are now beginning to prove – is the future of tackling the world's most complex diseases, from cancer to Parkinson's. Since the Human Genome Project (HGP) was completed in 2003, there's been a widely accepted belief that understanding genomics is the answer to curing cancer. But that's far from the view held by BERG co-founder, CEO and President Niven R. Narain. "Genomics is one component of biology," he explains.

BPM 31510 is just another cancer drug in human development trials, except for one thing. Scientists didn't toil away in labs to come up with it; artificial intelligence did. The cancer drug development process is costly and time-consuming. On average, it takes 24 to 48 months and upwards of 100 million to find a suitable candidate. Add that to the fact that 95% of all potential drugs fail in clinical trials, and the inefficiencies of the whole drug-discovery machine really become apparent.

BERG will be presenting interim data from its ongoing Phase I study of BPM 31510 (IV) in advanced solid tumors, which demonstrates the promise of metabolically-driven therapy by reversing the Warburg phenotype in cancer. BPM 31510 (IV) is one of the first cancer drugs guided in development by artificial intelligence. BPM 31510 (IV) appears to reverse the compromised metabolism of cancer cells which normalizes the cancer microenvironment to induce cell death. BERG's Interrogative Biology platform was used in its BPM 31510 (IV) Phase I study to discover molecular markers identifying patients more likely to have clinical benefit to the treatment applying a precision medicine approach to the trial.

Berg is taking its tech-enabled approach to drug development into the clinic. The biotech, which is known for making brash statements about its ability to slash preclinical timelines, has incorporated its machine-learning technology into a Phase II pancreatic cancer trial in an attempt to identify the patients who are most likely to respond to the treatment. In its early years, Berg, which was cofounded in 2006 by real estate billionaire Carl Berg, applied a combination of genomics, systems biology, computational modeling and artificial intelligence to the discovery of a pipeline of products. Now, with two candidates in the clinic in multiple indications, the biotech is aiming to use similar capabilities to improve its odds of success in human trials. A Phase II study combining Berg's lead candidate, BPM 31510, with gemcitabine is acting as an early testing ground for the concept.

This article was first published in the April 2016 issue of WIRED magazine. Be the first to read WIRED's articles in print before they're posted online, and get your hands on loads of additional content by subscribing online. In November 2013, more than 100 patients with cancer - including pancreatic, breast, liver and brain tumours - embarked on clinical trials involving BPM 31510, a drug discovered by an algorithm. The story of BPM 31510 begins with the extraction of biological data from healthy and cancerous tissue samples from over 1,000 patients. This data was then processed by artificial intelligence algorithms, which analysed it and suggested possible drug treatments. "We've essentially reversed the scientific method," says Niven R Narain, the 38-year-old president and co-founder of Berg, the Boston pharma startup which makes BPM 31510. "Instead of a preconceived hypothesis that leads us to do experiments and generate a particular type of data, we allowed the biological data from the patients to lead us to the hypotheses." Making an effective cancer-fighting drug is a notoriously difficult process: according to Narain, development and production can cost pharmaceutical companies up to 2.6 billion ( 1.8bn) and take 12 to 14 years to complete. "Only one per cent of the cancer drugs that make it to clinical trials prove to be effective. It's expensive and the development process is inexcusably long," Narain says.