This data-magic is known as “deep learning”, and it is a central component upon which all forms of AI — from self-driving cars, to Prisma app and Google Image Search — are built.

Back to cats. “The input could be a cat’s face,” explains Mehta. “Now there are multiple data-points in a cat’s face. The eye of a cat is different from the eye of a human or eye of a dog. The size of a cat is different. So the computer is programmed in such a way using the neural network to recognise the cat and provide the output of: ‘okay, that is the image of a cat.’

“If that sounds easy, it isn’t. That programming is not easy — it’s not linear programming. Way back we had this simple programming where you input this data and that is the outcome… but now computers have to analyse various data-points to recognise that pattern which is of a cat.”

Eventually the process of deep-learning, leads to what you or I would today label AI. “So, when the programming is done and when the system is trained to recognise the same problem — the cat in a video, or in a picture, or in real-life, this is where the computer becomes intelligent enough to recognise the cat anywhere. Even if you just show the eye or marker of a cat, it will immediately recognise it,” concludes Mehta.

The superhuman potential of AI, is something Zhavoronkov also talks about. During a Skype presentation he exhibits a series of slides demonstrating the speed at which this technology is emerging. “The real renaissance in this field started in kind of 2010-ish,” he says. “In 2015, deep learning surpassed human accuracy in image recognition, last year it came close to human accuracy in text recognition, human or almost superhuman accuracy in voice recognition.”

It is compelling stuff — but will it make money? As medical costs rise across the UAE, the USA and the world, can AI really save big pharma? How can relatively small companies, such as Insilico Medicine, hope to compete in an industry where the R&D for a single over-the counter-drug can cost billions of dollars?

There is a case to be made that many of these companies have become victims of their size. “Basically, they send emissaries to the jungle of Amazon and to all kinds of rare islands to find new molecules, and they construct libraries of those molecules consisting of millions of compounds and they blindly test those using huge robots, on human cell lines and on bacteria,” observes Zhavoronkov, referencing an Insilico’s recently-published paper which won the American Chemical Society Award in 2016.

Image courtesy Insilico Medicine.

In it, the Insilico team suggest a new drug delivery pipeline powered by AI. By using the above techniques, Zhavoronkov believes they can generate new molecules very quickly and test them in mice, and then in human models. “And you can essentially replace entire pharma companies,” he says. “That’s why you don’t need those huge robotic facilities anymore, you can have a small basement somewhere in the UAE where you have a lab to validate your predictions.”

By contrast, 95 percent of the clinical trials in cancer fail and often take decades,” Zhavoronkov points out. “So, most of the time pharmaceutical companies are used to failure.” This is why, he argues, companies can charge up to US$200,000 for the treatment of one patient, which costs them nothing, “because they want to recover their R&D costs.”

Mehta describes the “drug pipeline” process Insilico Medicine is working towards in three simple steps. “First, we collect actionable data, for example a blood test, a urine sample, or other indicators. Then we train a machine using neural networks to analyse that data over a period of time and based on that data the trained system can come up with a potential drug pipeline for various chronic diseases. The final stage is to validate this new drug in a clinical trial, and then place it in the market.

“The advantage of an AI is what would have taken 20 years to identify the right molecule, pass it through animal modelling to the pre-clinical stage, now the rate of failure and the duration is reduced.”

In many parts of the world the medical system is broken. What remains to be proven is whether or not AI really will revolutionise the drug pipeline, making faster and cheaper to produce effective drugs. As Zhavoronkov himself admits during our conversation, it is very likely that once Insilico Medicine validates its model, the existing pharma giants will simply swoop in and deliver a buyout offer the team can’t refuse.

This scenario is made more likely by the fact that many of the global pharmaceutical giants are home to a treasure trove of proprietary Big Data that is not in the public domain. Because this data is crucial for developing the sophistication of an AI engine through deep learning techniques, a market synergy between mainstream pharma and cutting-edge biotech looks likely in the future.

This will raise questions over the cost of drugs, corporate culture and the OpenAI standards that have been championed by the likes of Elon Musk.