A new drug usually starts with a tragedy. Born in what is now Zimbabwe, the child of a mechanic and a radiology technician, Ray fled with his family to South Africa during the Zimbabwean War of Liberation. He remembers the journey there in 1980 in a convoy of armored cars. As the sun blazed down, a soldier taught 8-year-old Ray how to fire a machine gun. But his mother kept having to stop.

Research into the therapeutic potential of psychedelic drugs was pioneered by psychiatrists way back in the 1950s, but the emergence of advanced technologies in pharma appears to have breathed new life into the field. As interest in the psychedelics market gains stream, a number of drug companies are now employing artificial intelligence (AI) methods in their search for new psychedelic compounds to treat a range of mental and physical conditions. One in four people in the UK will experience some kind of mental health problem every year, and figures are almost identical in the US. Despite this, treatments for psychological conditions are relatively limited – and for many patients, the drugs that are available come with side effects that negatively impact their quality of life. Psychedelics are hallucinogenic drugs that alter a person's perception and mood and affect their thought processes.

Prof. Yanay Ofran's amazing story about the pursuit of an antibody that will save the world from disease Shlomit Lan and Gali Weinreb Professor Yanay Ofran, founder and CEO of Biolojic Design, a company that develops smart antibodies designed to treat a variety of diseases, is frustrated. "Humanity invests $300 billion each year in drug development, and what do we get? At most, we get a few dozen medications a year, most of which don't solve the problems, and give an additional three weeks of life on average to patients with pancreatic cancer, or manage to inject a medication that to date was given via infusion. Those are the breakthroughs," he says despairingly. But Ofran does not think the pharmaceutical companies are the only culprit. "The drug companies are portrayed as a devil who says, 'I won't cure this because it's not worth my while.' But these companies do have a legal obligation towards their shareholders, not to develop drugs unless there's an economic incentive. The problem, as analyzed by Ofran, is much more complicated and therefore far more difficult to treat. "There are three players sitting around the drug development table: science, regulation and the business world.

The developed world is on the cusp of turning the corner in the fight against COVID-19 thanks to the unprecedented effort to rapidly develop and distribute effective vaccines. Now technologists are hoping to take drug development to the next level, and AI will play a big role. One of the companies at the forefront of using machine learning and AI to develop drugs is CytoReason. The company helps pharmaceutical firms like Pfizer accelerate drug development by providing high resolution models of the human body that's infected with the disease that the drug companies are targeting. "If I told you that in 200 years, drugs would be developed in a computer, you would not be real surprised," said CytoReason CEO and founder David Harel.

Daphne Koller is best known as the cofounder of Coursera, the open database for online learning that launched in 2012. But before her work on Coursera, she was doing something much different. In 2000, Koller started working on applying machine learning to biomedical data sets to understand gene activity across cancer types. She put that work on hold to nurture Coursera, which took many more years than she initially thought it would. She didn't return to biology until 2016 when she joined Alphabet's life science research and development arm Calico.

The pharmaceutical regulatory environment is becoming more challenging, and drugs must go through extensive testing before they hit the market. As a result, there are major incentives for drug companies to reduce R&D spending in order to free up funds for additional ventures and offer lower prices for their products. By adopting sophisticated data science, and machine learning, pharmaceutical researchers can save money and time on R&D. On top of that, machine learning technology provides new ways for drug companies to streamline nearly every other aspect of their businesses. To outline how machine learning can be best applied, data science software company, Dataiku, has recently released a whitepaper titled "How Machine Learning is Transforming Pharmaceuticals".

What does the youngest chief executive in Big Pharma want? When Vas Narasimhan, 41, took the helm of drug giant Novartis in February, he'd already put the project in motion. Novartis scouts were dispatched to visit air traffic control towers and the Swiss electrical grid to see how other industries dealt with torrents of data. Working with McKinsey's QuantumBlack unit, they built a software system called Nerve that not only keeps track of every data point on all 550 clinical trials testing Novartis drugs, but also uses analytic software to predict potential hiccups in the execution of those studies. Soon Narasimhan will be able to walk into mission control at the company's Basel, Switzerland, headquarters and call up whatever information he needs in an instant. "When you look at history, it takes the medical establishment 50 to 75 years to actually change how we do clinical studies," Narasimhan says.

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.

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.

Numerous companies in the healthcare space are experimenting with artificial intelligence, but what does the future hold in this sphere? You'll probably know IBM's supercomputer, Watson, from its 2011 appearance on Jeopardy. Up against two of the US quiz show's longest-running and highest-earning contestants, Watson clinched a 1m prize after answering a series of quick-fire general knowledge questions. It wasn't a close call either – at the final score, Watson's total was 31,547 ahead of its rivals' combined. But in the five years since, IBM's supercomputer has been working towards another goal, one far more lucrative than the Jeopardy!