An enormous figure looms over scientists searching for new drugs: the estimated US$2.6-billion price tag of developing a treatment. A lot of that effectively goes down the drain, because it includes money spent on the nine out of ten candidate therapies that fail somewhere between phase I trials and regulatory approval. Few people in the field doubt the need to do things differently. Leading biopharmaceutical companies believe a solution is at hand. Pfizer is using IBM Watson, a system that uses machine learning, to power its search for immuno-oncology drugs.

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.

If a group of chemists found 18 more potent versions of a drug out of a sea of 3,000 potential chemicals in the span of a few weeks, they might be hailed as superhumans. That actually happened at Relay Therapeutics, said Dr. Donald Bergstrom, the company's head of R&D. But the driving force behind it wasn't human at all -- it was artificial intelligence. AI and machine learning have been hailed as a powerful new tool for drug discovery. But despite the hype, there is still a huge gap between the potential and the reality.

An enormous figure looms over scientists searching for new drugs: the estimated US$2.6-billion price tag of developing a treatment. A lot of that effectively goes down the drain, because it includes money spent on the nine out of ten candidate therapies that fail somewhere between phase I trials and regulatory approval. Few people in the field doubt the need to do things differently. Leading biopharmaceutical companies believe a solution is at hand. Pfizer is using IBM Watson, a system that uses machine learning, to power its search for immuno-oncology drugs.

Creating innovative, lifesaving medicines, say pharmaceutical company bosses, requires a sufficient return on investment. In 2017, according to Deloitte, the 12 largest bio- pharma companies got a mere 3.2% return out of their drug-research arms. In 2010, that number was 10.1%. How can pharma break out of this rut? One avenue might be the use of artificial intelligence to improve drug discovery at the earliest stages (when the risk of failure is also the highest).

On a recent Friday in Boston, Randell Sanders gave a nurse two samples of his blood, plus a sample of urine and saliva. Clinicians would test some of the samples to see how he is responding to treatment for pancreatic cancer. But samples also were sent to a lab where computers using artificial intelligence are changing the way pharmaceutical companies develop drugs. The idea is that machines, which are adept at pattern recognition, can sift through vast amounts of new and existing genetic, metabolic and clinical information to unravel the complex biological networks that underpin diseases. That, in turn, can help identify medications likely to work in specific patient populations, while simultaneously steering companies away from drugs that are likely to fail.

When researchers used to try to diagnose and treat diseases, they would often search for one mutation on a single gene that was causing the problem. Or maybe they would look for average effects of a mutation that led to a disease across the entire population. But these approaches ignored the complexities and specifics that truly give rise to disease -- demographic information, proteins, multi-gene interactions, environmental effects, and a whole host of other facets. Until recently, computers weren't powerful enough to be able to analyze all of this health information, nor were there large enough datasets to test. But the rise of Artificial Intelligence (AI) can tease out interactions from big health data that is emerging from the ability to quickly sequence entire genomes and gather more molecular information than ever before. AI could make precision medicine a reality, since it will hopefully one day be able to identify the unique characteristics an individual has that could lead to certain diseases, and how to treat them.

Berg Health's cofounder and chief executive Niven R. Narain is used to being laughed out of rooms, given his interest in bringing artificial intelligence into the drug development process. But things have changed, he says, thanks to growing excitement around the practice known as precision medicine. This afternoon, at the Fast Company Innovation Festival, Narain spoke on a panel on the topic of bringing advanced technologies to medicine with industry experts from Mount Sinai and Columbia University. Precision medicine is an all-encompassing term, which broadly refers to the idea of treating patients in a more personalized, targeted way rather than taking a one-size-fits-all approach to disease. The White House announced a $215 million investment in precision medicine earlier this year; if nothing else, it generated a lot of hype.

The U.S. government wants to find out if artificial intelligence can help doctors diagnose and treat breast cancer more effectively. In an effort to find targeted treatments for particularly invasive types of breast cancer that don't respond well to existing drugs, the Department of Defense announced this week that it is enlisting the biopharma company Berg Health to use AI for drug discovery. The partnership supports the White House's Cancer Moonshot initiative to screen up to 250,000 patient samples in search of new biological indicators, or biomarkers, of the earliest signs of cancer. While the death rate from breast cancer has dropped steadily over the past two decades, it remains the second-biggest killer among cancers in U.S. women, according to the National Cancer Institute. Under the partnership, Berg will have access to the DoD's Clinical Breast Care Project, a bank of 13,600 samples of both healthy and diseased tissue from nearly 8,000 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.