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.

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.

If I see a robot coming to kill me, I'll just look for the off-switch or how I can unplug it". I'm ever so slightly paraphrasing Pete Trainor, one of yesterday's speakers at Wired Health 2018, but this was the jist of what he said to me over a coffee as we discussed AI, robotics and its place in the the world. And if I can draw a theme from yesterday's excellent Wired Health event held at the very impressive Francis Crick Institute, it's that AI, robotics and digital technology are not here to replace people in healthcare provision. Rather, they are here to augment and "scale-up" the amount that a healthcare professional can do. This was a theme which was alighted on by a number of speakers.

London-based biotech business BenevolentBio is on a mission to overcome the failure of the drug industry to innovate on drug discovery. Andrew Huddart explains and talks to its chief executive, Jackie Hunter. The tools of big data are helping to reshape the industrial landscape of the 21st century. They are helping make entire economic activities obsolete and helping give rise to new ones. The area of health and drug discovery in particular is far from excluded from this change.

BenevolentBio's CMO, Dr Patrick Keohane, says the industry is on the cusp of an artificial intelligence (AI) revolution… Medical research is entering a period of rapid transformation, driven by the explosion of scientific data, rapid advances in artificial intelligence (AI), and the development of extremely powerful computers. This convergence enables researchers to access enormous and diverse datasets to rapidly form and test scientific hypotheses. It is disrupting the way we identify, validate, and transform scientific concepts into potential Healthcare solutions – ultimately revolutionising the scientific process. AI enables access and the more effective use of existing information for the discovery and development of better drugs at a speed previously unimagined. This is helping to meet the high demand from society to provide better medicines at a reasonable cost, but also influencing how we are looking to treat rarer forms of disease.

An IBM's executive Deborah DiSanzo just announced a collaboration with a pharmaceutical giant Pfizer to speed up anticancer drug discovery. This is yet another sign of a technological transformation unfolding in pharmaceutical industry. The newly formed partnership will bring the power of IBM's supercomputer Watson and its artificial intelligence system to help researchers at Pfizer advance "immuno-oncology", a potentially promising area for cancer research. Pfizer will use Watson's capabilities of machine learning, natural language processing, and other cognitive reasoning technologies to improve analysis of massive volumes of public and private datasets, including more than 30 million sources of laboratory and data reports, research articles, patents, and other medical literature. It is supposed to assist in testing research hypotheses and identify new promising therapeutic targets.

Remember the ice bucket challenge? The meme of 2014 brought a splash (ha) of attention to amyotrophic lateral sclerosis, also known as ALS, the neurodegenerative disease Stephen Hawking lives with along with some 450,000 other people around the world. The American ALS Association (ALSA) received an outpouring of donations, which directly led to the discovery of a gene that contributes to the disease. Now, artificial intelligence is set to bring the next breakthrough in the fight against ALS -- specifically regarding the development of drugs. Jackie Hunter, CEO of BenevolentBio, said at the TechCrunch Disrupt conference on Tuesday in London that her company expects to make a difference for ALS patients in the next five years because of pharmaceuticals that could be developed based on the findings of artificial intelligence that scans the oceans of medical studies being published every day.

What if the drug that could effectively treat amyotrophic lateral sclerosis, the paralyzing neurological disease that skyrocketed into the public consciousness thanks to the ice bucket challenge, already exists? Hunter runs the biomedical arm of the artificial intelligence firm BenevolentAI, applying machine learning to vast databases of medical research to rapidly scan and organize the data. It seems unlikely that rehashing scientific research could lead to new discoveries, but new studies on life sciences are published every 30 seconds, so valuable research is often missed. Hunter told TechCrunch today at Disrupt London that BenevolentBio's AI has already succeeded. BenevolentBio's AI scanned for studies that might hold secrets about ALS treatment.

What if the drug that could effectively treat amyotrophic lateral sclerosis, the paralyzing neurological disease that skyrocketed into the public consciousness thanks to the ice bucket challenge, already exists? Hunter runs the biomedical arm of the artificial intelligence firm BenevolentAI, applying machine learning to vast databases of medical research to rapidly scan and organize the data. It seems unlikely that rehashing scientific research could lead to new discoveries, but new studies on life sciences are published every 30 seconds, so valuable research is often missed. Hunter told TechCrunch today at Disrupt London that BenevolentBio's AI has already succeeded. BenevolentBio's AI scanned for studies that might hold secrets about ALS treatment.

Please can you give an overview of the current drug discovery paradigm? In what ways do you think it needs to be leaner? With the current drug discovery paradigm, it takes up to 15 years to translate an idea, such as hypothesizing a certain protein is important in a disease and testing this with targeting the protein with a drug, all the way through to proof of concept. The drug has to be filed with the regulatory authorities, having done all the safety and efficacy testing. Estimates vary, but it's currently reckoned to cost over 1 billion dollars per drug.