If there's one thing we can all agree upon, it's that the 21st century's captains of industry are trying to shoehorn AI into every corner of our world. But for all of the ways in which AI will be shoved into our faces and not prove very successful, it might actually have at least one useful purpose. Risk mitigation isn't a sexy notion but it's worth understanding how common it is for a new drug project to fail. To set the scene, consider that each drug project takes between three and five years to form a hypothesis strong enough to start tests in a laboratory. A 2022 study from Professor Duxin Sun found that 90 percent of clinical drug development fails, with each project costing more than 2 billion.

Artificial Intelligence (AI) in drug discovery is now on a steep rise. A growing number of companies compete to develop new drugs faster, cheaper, and with a much higher success rate by using AI tools at all crucial stages of the drug discovery pipeline. Most of the players in this quickly expanding market are oriented towards big pharma, which is routinely investing billions into drug development. Such a partnership is tempting not only for startup companies but also for established leaders in the field of AI-based drug development because it provides stable multi-year contracts backed up by the financial resources and infrastructure of the pharmaceutical giants. As a result, end-to-end AI-based drug discovery services are tailored for large corporate customers.

Shannon Flynn is a freelance blogger who covers education technologies, cybersecurity and IoT topics. You can follow Shannon on Muck Rack or Medium to read more of her articles. Artificial intelligence (AI) has had major impacts in the health care industry, affecting providers and patients alike. However, it also aids the pharmaceutical sector in finding and creating the drugs that support health care through the treatment and management of diseases. Here's a closer look at some positive changes AI could bring this year.

Data science and artificial intelligence are adding a new dimension to drug discovery and development, emphasizing computation and machine learning. Given this shift, pharmaceutical companies are actively building infrastructure, data, tools, and teams to bring together data scientists with biology and life science experts. Pharma and biotech innovation offer a glimpse into how large organizations integrate AI tools and techniques with traditional subject matter experts who possess a deep understanding of the underlying problems to be solved. To gain an insider's perspective on how pharma companies use AI and machine learning, I invited Dr. Bülent Kızıltan to join episode #717 of the CXOTalk series of conversations with people shaping our world. He is Head of Causal & Predictive Analytics, Data Science & AI, at the Novartis AI Innovation Center.

Mati Gill's exposure to the power of pharma collaboration can't be overstated. During his 11 years as COO of the Global Legal Group, and later, head of government affairs, corporate & international markets at Teva Pharmaceuticals, he was exposed to virtually every aspect of the business. When Teva made a concerted effort to build its corporate innovation strategy to strengthen its development platforms and pipeline, he was an undisputed pick to lead the exercise in support of Teva R&D for the Israeli pharma titan. As he helped make inroads with Israeli's academic and emerging life sciences ecosystems, a promising opportunity began to reveal itself and became the focus of his work: The roles of computational biology and artificial intelligence (AI) in drug discovery and development. Gill found the concept nascent among the next generation of innovators but hampered by the pharmaceutical industry at large.

The race to develop vaccines and treatments for the coronavirus pandemic is entering a crucial stage with large-scale trials under way. But with seemingly little oversight on pricing, some experts are concerned that pharmaceutical companies could end up charging way too much for COVID-19 treatments. And if rich nations corner the market on vaccines, they could also limit accessibility for low-income countries. Plus, Twitter found itself at the centre of a hack that compromised its high-profile users' accounts. We talk to the CEO of a Silicon Valley company that uses biometrics and artificial intelligence to protect accounts.

Cambridge, UK-based biotech startup Mogrify, which is working on systematizing the development of novel cell therapies in areas such as regenerative medicine, has closed an initial $16 million Series A. The raise follows a $4M seed in February -- taking its total raised to date to $20M. Put simply, Mogrify's approach entails analysis of vast amounts of genomic data in order to identify the specific energetic changes needed to flip an adult cell from one type to another without having to reset it to a stem cell state -- with huge potential utility for a wide variety of therapeutic use-cases. "What we're trying to do with Mogrify is systematize that process where you can say here's my source cell, here's my target cell, here are the differences between the networks… and here are the most likely points of intervention that we're going to have to make to drive the fate of an adult cell to another adult cell without going through a stem cell stage," says CEO and investor Dr Darrin Disley. So far he says it's successfully converted 15 cells out of 15 tries. "We're now rapidly moving those on through our own programs and partnership programs," he adds.

In a world first, Insilico Medicine, a Hong Kong-based startup developing deep neural networks for drug discovery, has successfully synthesized and pre-clinically validate a drug candidate in just 25 days, making the drug discovery process, including the designing stage, take about 46 days. According to Insilico's research team and its collaborators at the University of Toronto, the method of designing new kinds of molecules by using a deep generative artificial intelligence (AI) model – called generative tensorial reinforcement learning (GENTRL) – not only set a record time compared to traditional methods but also proved to be 15 times faster than a typical pharma corporation's efficient R&D process. It's worth pointing out, especially for readers unfamiliar with the big pharmaceutical industry, that it takes more than a decade and millions of dollars to discover and develop a drug candidate. What's even more depressing about this inefficient industry that keeps passing off the illusion of innovation for real innovation, is that in the last twenty-plus years the success rate for a drug candidate entering Phase I trials have stagnated at under 10%. Meanwhile, in pre-clinical phases the failure rates for new compounds is over 99%.

My nose is pressed to the glass as I watch thousands of human cellular samples whisk by, running through a large, elaborate machine. There are lab workers bustling around in neatly pressed lab coats, jotting down notes on their clipboards. It looks like a scene from a futuristic movie. This is real life, and the technology in use is poised to change the world of healthcare as we know it. "I never wanted to work for a pharmaceutical company," says Amanda Guisbond, the director of corporate communications at Recursion Pharmaceuticals.

Machine-learning technology has beaten humans at games of chess and Go to worldwide fanfare. A demonstration of its eerily lifelike prowess in making phone calls to unsuspecting people went viral. But a less-noticed win for DeepMind, the artificial-intelligence arm of Google's parent Alphabet Inc., at a biennial biology conference could upend how drugmakers find and develop new medicines. It could also dial up pressure on the world's largest pharmaceutical companies to prepare for a technological arms race. Already, a new breed of upstarts are jumping into the fray.