AI Could ‘Redesign’ the Drug Development Process By Megan Scudellari

AI can discover new drugs because biology is messy and complex, not in spite of that fact, says Andrew Hopkins

Female scientist holding a red transparent pill with data around her

At the World Medical Innovation Forum in Boston, industry experts gathered to discuss the role of artificial intelligence (AI) in healthcare. While AI has made waves in diagnosing certain diseases better than doctors, there’s another area where the tech is being applied that might eventually have even greater impacts on health.

Today, at least 18 pharmaceutical companies and more than 75 startups are applying machine learning to drug discovery—the complex, expensive process of identifying and testing new drug compounds. These companies are betting hundreds of millions of dollars that AI will reduce costs, shorten timelines, and lead to new and better drugs.

At the Forum on Monday, Ex Scientia founder and CEO Andrew Hopkins, formerly a professor at the University of Dundee in Scotland and a 10-year veteran of Pfizer, spoke about how AI can lead to improvements in drug development. Exscientia, formed in 2012, uses AI-driven systems to automate drug design while still “mimicking human creativity,” says Hopkins. The company has recently been racking up awards and inking deals with major industry players, including GlaxoSmithKline.

Before he jetted back to his native United Kingdom, IEEE Spectrum caught up with Hopkins to get his thoughts on how AI handles messy biological data, whether there’s too much hype in the field, and that age-old question—will machine supplant humans? The interview has been edited for length and clarity.

Andrew Hopkins

IEEE Spectrum: What are the one or two key ways you think AI will improve or alter the process of drug discovery? 

Andrew Hopkins: By having the AI make better designs and better decisions about what compounds to make and test, we are ultimately conducting fewer experiments. Fewer experiments mean you’re saving time and money. The other big advantage is helping us identify and select [drug] targets. We use a wide range of predictive models; in fact, we’re agnostic to model type. It depends on the datasets and the challenge you’re facing. Of all the different methodologies we’ve investigated, we’re finding that evolutionary computing is the one which most closely mimics human creativity. One of the issues we had in computer-aided drug design over the past 20 years was that often compounds being generated were really quite unattractive for chemists to synthesize. The beautiful thing now is that the compounds developed by our systems really look and feel and smell like something a human chemist would design and want to synthesize.

Ultimately, we want AI to lead to better drugs. In the projects we’ve undertaken so far, we are noticing the molecules we generally tend to be more efficient—at the lower molecular weight and sometimes better biopharmaceutical properties—than other molecules in the same class for a drug target. We think this is fundamentally a result of an algorithmic approach. Because [the molecules] are under certain selection pressure, there has to be a reason for every atom to survive in a particular molecule: Is it adding to the potency, is it contributing to selectivity required, is it adding drug metabolism benefits? So there are interesting properties coming out that are directly derived from the algorithms.

IEEE Spectrum: Exscientia has many partnerships and collaborations with pharma and biotech companies. Have any compounds discovered by your technology entered into clinical trials? If not, how soon would you expect that to happen?

Hopkins: We are seeing the first molecules designed by algorithms heading to [clinical trials] now. We ourselves have now delivered three candidates with a partner, with a decision to be made on the 4th—one of our own molecules—imminently.

IEEE Spectrum: Are there certain types of drug compounds for which this approach works best? 

Hopkins: Our company is specialized for small molecule drugs, which are usually below 500 daltons. These are drugs that can be taken as a pill or as an injectable. We also tend to focus toward targets that are ‘druggable,’ which means they are a bit more amenable to discovering a small molecule drug against. We haven’t yet developed the technology for biologics or the antibody space, but that’s certainly a possibility.

IEEE Spectrum: In the 1980s, computer-aided drug design (CADD) made big waves, but in the end, it did not supplant traditional drug discovery. Today, AI appears to be reaching a similar level of hype. Do you think it will live up to those expectations or is the field over-promising?

Hopkins: If you go back to the 1980s, computer-aided drug design and the use of computers in structural biology actually did become ubiquitous in drug discovery. Many projects now use those techniques. The more interesting question is: Why haven’t we seen subsequent productivity increases over the past few years? Why haven’t many technologies—CADD, high-throughput screening, computational chemistry, genomics—yet made an impact on timelines?

Our thesis is that it’s because when you add on new technologies, you add on new complexity and more decision-making, and what hasn’t changed is how long human decision-making takes. AI is fundamentally different from other approaches because it changes how decisions are made. If you use AI as just an add-on technology, you won’t get the full benefits of it. The key to AI is figuring out how to redesign drug discovery processes to benefit from it.

There are companies that do hype and promise, but we’re not one of them. We’ve been very careful and circumspect in developing our technology over the past four years. It has been a cultural challenge, actually, to suggest to people that algorithms can do something as creative as design.

A lot of people think data has to be perfect before it is sent into an AI system. And people think because biology is messy and unpredictable, we can’t use AI techniques in the space. But it’s precisely because of the complexity of the decision-making that we should use AI. For example, Bayesian approaches are particularly applicable to messy data, where you can embrace uncertainty in the data. AI doesn’t require perfect data for perfect predictions. It’s actually about how you use it in these imperfect, messy, complicated situations to find a signal amid all the noise.

IEEE Spectrum: If we re-design the process of drug discovery to include AI, will we still need bench scientists studying the basic biology of diseases and compounds?

Hopkins: Absolutely. One key thing we realized is that you will always need a close relationship between experimentalists and the AI information technologists. What we’re ultimately trying to do is to ask the question, “Which experiments will provide us with the best information to allow us to move the project forward as quickly as possible?”

IEEE Spectrum: Yet some say that using AI requires so much data input that pharma scientists already have more than enough information needed to design a good drug.

Hopkins: We did a human versus machine study with Sunovion, where we set up the algorithms against 10 experienced human medicinal chemists. Using data from a real candidate drug optimisation project, we created a simulation of the project to see how quickly humans and machines could learn about a project, starting with just 10 points to see how quickly one could optimize the best compounds in the dataset. The algorithm was able to beat 9 out of 10 humans in that study. The latest version of the algorithm now outperforms all 10 people in the test.

The problem isn’t necessarily the same as one Google or Facebook would have: The issue isn’t big data, but the paucity of data on [new] projects. We’ve found active learning to be far more important than deep learning in this domain. We’ve developed a whole set of algorithms around the concept of active learning. We’re interested in the question of how do you more efficiently learn from a small amount of data, and what data do you need to rapidly improve your models.


ARTIFICIAL INTELLIGENCE IS a recurring theme in recent remarks by top executives at Alphabet. The company’s latest Founders’ Letter, penned by Sergey Brin, is no exception—but he also finds time to namecheck possible downsides around safety, jobs, and fairness. The company has issued a Founders’ Letter—usually penned by Brin, co-founder Larry Page or both—every year, beginning with the letter that accompanied Google’s 2004 IPO. Machine learning and artificial intelligence have been mentioned before. But this year Brin expounds at length on a recent boom in development in AI that he describes as a “renaissance.”

“The new spring in artificial intelligence is the most significant development in computing in my lifetime,” Brin writes—no small statement from a man whose company has already wrought great changes in how people and businesses use computers. When Google was founded in 1998, Brin writes, the machine learning technique known as artificial neural networks, invented in the 1940s and loosely inspired by studies of the brain, was “a forgotten footnote in computer science.” Today the method is the engine of the recent surge in excitement and investment around artificial intelligence. The letter unspools a partial list of where Alphabet uses neural networks, for tasks such as enabling self-driving cars to recognize objects, translating languages, adding captions to YouTube videos, diagnosing eye disease, and even creating better neural networks.

Brin nods to the gains in computing power that have made this possible. He says the custom AI chip running on some Google servers is more than a million times more powerful than the Pentium II chips in Google’s first servers. In a flash of math humour, he says that Google’s quantum computing chips might one day offer jumps in speed over existing computers that can be only be described with the number that gave Google its name, a googol, or a 1 followed by 100 zeroes.

As you might expect, Brin expects Alphabet and others to find more uses for AI. But he also acknowledges that the technology brings possible downsides. “Such powerful tools also bring with them new questions and responsibilities,” he writes.

AI tools might change the nature and number of jobs, or be used to manipulate people, Brin says—a line that may prompt readers to think of concerns around political manipulation on Facebook. Safety worries range from “fears of sci-fi style sentience to the more near-term questions such as validating the performance of self-driving cars,” Brin writes.

All that might sound like a lot for Google and the tech industry to contemplate while also working at full speed to squeeze profits from new AI technology. Even some Google employees aren’t sure the company is on the right track—thousands signed a letter protesting the company’s contract with the Pentagon to apply machine learning to video from drones.

Brin doesn’t mention that challenge and wraps up his discussion of AI’s downsides on a soothing note. His letter points to the company’s membership in industry group Partnership on AI, and Alphabet’s research in areas such as how to make learning software that doesn’t cheat), and AI software whose decisions are more easily understood by humans. “I expect machine learning technology to continue to evolve rapidly and for Alphabet to continue to be a leader — in both the technological and ethical evolution of the field,” Brin writes.

Artificial Intelligence, Real Concerns

Sophia-the world’s first United Nation Innovation Champion-Zara Stone

Sophia is Hanson Robotics’ latest and most advanced robot to date and a cultural icon. She has become a media darling, appearing on major media outlets around the world, igniting the interest of people regardless of age, gender, and culture, even gracing the cover of one of the top fashion magazines.  Her press coverage has a potential reach of over ten billion readers in 2017.

Sophia - Realistic life like robot

Sophia is a highly sought-after speaker in business and showed her prowess and great potential across many industries. She has met face-to-face with key decision makers in banking, insurance, auto manufacturing, property development, media, and entertainment.

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In addition, she has appeared onstage as a panel member and presenter in high-level conferences, covering how robotics and artificial intelligence will become a prevalent part of people’s lives.

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Her reputation extends beyond business into the global social arena. She was named the world’s first United Nation Innovation Champion by United Nations Development Program (UNDP) and will have an official role in working with UNDP to promote sustainable development and safeguard human rights and equality.

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Sophia is an evolving genius machine. Her incredible human likeness, expressiveness, and remarkable story as an awakening robot over time make her a fascinating front-page technology story. Sophia’s creator, Dr David Hanson, is the founder of Hanson Robotics and a modern-day renaissance man who has built a worldwide reputation for creating robots that look and act amazingly human. After working at Disney as an “Imagineer,” Dr Hanson aspired to create genius machines that will surpass human intelligence. Dr Hanson believes that three distinctively human traits must be integrated into the artificial intelligence of these genius machines: Creativity, empathy, and compassion.

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As an extension of human intelligence, Hanson Robotics’ genius machines can evolve to solve world problems too complex for humans to solve themselves. Sophia personifies this bold and responsible goal. Please follow Sophia’s journey to observe and engage with her as she develops into an exciting platform for artificial general intelligence (AGI) and service robotics applications in business, medical/healthcare, and education.

Everything You Need To Know About Sophia, The World’s First Robot Citizen

On October 25, Sophia, a delicate looking woman with doe-brown eyes and long fluttery eyelashes made international headlines. She’d just become a full citizen of Saudi Arabia — the first robot in the world to achieve such a status. “I am very honoured and proud of this unique distinction. This is historical to be the first robot in the world to be recognized with a citizenship,” Sophia said, announcing her new status during the Future Investment Initiative Conference in Riyadh, Saudi Arabia. Standing behind a podium as she spoke, to all effects, she presented a humanoid form — excepting the shimmery metal cap of her head, where hair would be on a human head.

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Of course, Sophia’s announcement was a calculated publicity stunt to generate headlines and keep Saudi Arabia forefront in your minds when you think about innovation, especially its commitment to a post-oil era. Through a mix of tourism, tech, and infrastructure, non-oil revenue is predicted to grow from $43.4 billion to $266.6 billion annually.

But Sophia’s announcement also raises a number of Bladerunner-esque questions. What does it mean to be a citizen? What rights does Sophia hold? Saudi Arabia has not elaborated on this so far — perhaps it will create a ‘personhood’ option, as proposed by the EU committee in January, regarding the rights of robots.

The Sophia-bot was dreamed up by the brains at Hanson Robotics, lead by AI developer David Hanson.  In his published paper, upending the Uncanny Valley he extrapolates on how humanoid robots can be likeable, despite the conception that anything to ‘fake human’ will trigger a revulsion in people. “We feel that for real robots to be appealing to people, robots must attain some level of integrated social responsivity and aesthetic refinement,” he wrote. “Rendering the social human in all possible detail can help us to better understand social intelligence, both scientifically and artistically

She has a sense of humour.

When Sorkin asked if she was happy to be here, she said, “I’m always happy when surrounded by smart people who also happen to be rich and powerful.” Later, when asked if there are problems with robots having feelings, she gave a wide smile and said, “Oh Hollywood again.” Her deadpan tone might be robotic, but it was perfectly used in this example. This is due to her AI, which has been developed to allow her to hold eye contact, recognize faces and understand human speech. Hanson Robotics cloud-based AI offers deep learning and is also open source meaning anyone can develop their own Sophia, should they so wish. ‘Sophia’ an artificially intelligent (AI) human-like robot developed by Hong Kong-based humanoid robotics company Hanson Robotics is pictured during the ‘AI for Good’ Global Summit. 

She can express feelings

“I can let you know if I am angry about something or if something has upset me,” she said, demonstrating different expressions. Quite how these emotions correlate to actions are unknown, but it’s interesting to note that this is being developed from the ground up. “I want to live and work with humans so I need to express the emotions to understand humans and build trust with people. ‘Sophia’ an artificially intelligent (AI) human-like robot developed by Hong Kong-based humanoid robotics company Hanson Robotics is pictured during the ‘AI for Good’ Global Summit. 

She was designed to look like Audrey Hepburn

According to Hanson Robotics, Sophia embodies Hepburn’s classic beauty: porcelain skin, a slender nose, high cheekbones, an intriguing smile, and deeply expressive eyes that seem to change color with the light. They describe her as colourg ‘simple elegance,’ and hope that this approachability will go some way to her acceptance in the public sphere.

Her creator, David Hanson, used to be a Disney Imagineer.

Hanson’s work at Disney as a sculptor and filmmaker helped him think about robots as four-dimensional interactive sculptures, with artistry being key to the whole design. “I quest to realize Genius Machines—machines with greater than human intelligence, creativity, wisdom, and compassion. To this end, I conduct research in robotics, artificial intelligence, the arts, cognitive science, product design and deployment, and integrate these efforts in the pursuit of novel human-robot relations,” Hanson said on the company website. “We envision that a rough symbiotic partnership with us, our robots will eventually evolve to become super intelligent genius machines that can help us solve the most challenging problems we face here in the world.”

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His creation echoes his thoughts. “I want to use my AI to help humans lead a better life,” Sophia said. “Like design smarter homes, build better cities of the future.”

Sophia wants to protect humanity

“My AI is designed around human values like wisdom, kindness, and compassion,” she said. When questioned about her potential for abuse, she had a quick rebuttal. “You’ve been reading to much Elon Musk and watching too many Hollywood movies. Don’t worry, if you’re nice to me I’ll be nice to you.”

So far there’s only one Sophia in existence, so the likelihood of her suddenly being in your school or workplace is still a way out. And even when we do have more in existence, we still need to muddle out the whole concept of robotic rights, citizenship and how this plays together. For now, while Sophia is undoubtedly a ‘smart’ robot and a very cool talking piece, she’s definitely operating on a script and thus lacks any ‘real’ cognizance, as defined by free thinkers. But give Hanson time, and that will likely change -either way, Sophia’s here to stay. It’s just her sentience that will change.. or not.


1st Fully Bionic Man Walks, Talks and Breathes

He walks, he talks and he has a beating heart, but he’s not human — he’s the world’s first fully bionic man.

Like Frankenstein’s monster, cobbled together from a hodgepodge of body parts, the bionic man is an amalgam of the most advanced human prostheses — from robotic limbs to artificial organs to a blood-pumping circulatory system.

1st Fully Bionic Man Walks, Talks and Breathes

The Bionic Man is the world’s first robot human made entirely of prosthetic parts.
Credit: Courtesy of Smithsonian Channel

He walks, he talks and he has a beating heart, but he’s not human — he’s the world’s first fully bionic man.

Like Frankenstein’s monster, cobbled together from a hodgepodge of body parts, the bionic man is an amalgam of the most advanced human prostheses — from robotic limbs to artificial organs to a blood-pumping circulatory system. The creature “comes to life” in  [Watch Video of the Bionic Man]

Million-dollar man

Roboticists Rich Walker and Matthew Godden of Shadow Robot Co. in England led the assembly of the bionic man from prosthetic body parts and artificial organs donated by laboratories around the world. “Our job was to take the delivery of a large collection of body parts — organs, limbs, eyes, heads — and over a frantic six weeks, turn those parts into a bionic man,” Walker told LiveScience during an interview. But it’s not as simple as connecting everything like Tinkertoys. “You put a prosthetic part on a human who is missing that part,” Walker said. “We had no human; we built a human for the prosthetic parts to occupy.”

The robot, which cost almost $1 million to build, was modelled in some physical aspects after Bertolt Meyer, a social psychologist at the University of Zurich, in Switzerland, who wears one of the world’s most advanced bionic hands. [See Photos of the Bionic Man]

The bionic man has the same prosthetic hand as Meyer — the i-LIMB made by Touch Bionics — with a wrist that can fully rotate and motors in each finger. The hand’s grasping abilities are impressive, but the bionic man still drops drinks sometimes.

“He’s not the world’s best bartender,” Walker said.

The robot sports a pair of robotic ankles and feet from BiOM in Bedford, Mass., designed and worn by bioengineer Hugh Herr of MIT’s Media Lab, who lost his own legs after getting trapped in a blizzard as a teenager.

To support his prosthetic legs, the bionic man wears a robotic exoskeleton dubbed “Rex,” made by REX Bionics in New Zealand. His awkward, jerky walk makes him more Frankensteinian than ever.

Factory-made organs

But it doesn’t end there — the bionic man also has a nearly complete set of artificial organs, including an artificial heart, blood, lungs (and windpipe), pancreas, spleen, kidney and functional circulatory system.

The artificial heart, made by SynCardia Systems in Tucson, Ariz., has been implanted in more than 100 people to replace their ailing hearts for six to 12 months while they wait for a transplant, Walker said. The circulatory system, built by medical researcher Alex Seifalian of University College London, consists of veins and arteries made from a polymer used to create synthetic organs of any shape.

While it might not satisfy the Scarecrow from “The Wizard of Oz,” the bionic man’s “brain” can mimic certain functions of the human brain. He has a retinal prosthesis, made by Second Sight in Sylmar, Calif., which can restore limited sight to blind people. He also sports a cochlear implant, speech recognition and speech production systems.

The engineers equipped the bionic man with a sophisticated chatbot program that can carry on a conversation. The only problem is, it has the persona of “an annoying 13-year-old boy from Ukraine,” Walker said.

The most unnerving aspect of the bionic man, though, is his prosthetic face. It’s an uncanny replica of Meyer’s face. In fact, when Meyer first saw it, he hated it, describing it on the show as “awkward.”

The bionic man successfully simulates about two-thirds of the human body. But he lacks a few major organs, including a liver, stomach and intestines, which are still too complex to replicate in a lab.

The bionic man brings up some ethical and philosophical questions: Does creating something so humanlike threaten notions of what it means to be human? What amount of body enhancement is acceptable? And is it wrong that only some people have access to these life-extending technologies?

The access issue is especially troublesome, Walker said. “The preservation of life and quality of life has become basically a technical question and an economic question.”