How AI Supercharges Drug Research

Machine learning can quickly analyze millions of compounds, helping researchers bring drugs to clinical trials sooner.

The following is an excerpt from The Singularity Is Nearer: When We Merge with AI by Ray Kurzweil.

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The singularity is nearer: Ray Kurzweil

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The Singularity Is Nearer: When We Merge with AI


When you take your car to the shop to get it fixed, the mechanic has a full understanding of its parts and how they work together. Automotive engineering is effectively an exact science. Thus, well-maintained cars can last almost indefinitely, and even the worst wrecks are technically possible to repair. The same is not true of the human body. Despite all the marvelous advances of scientific medicine over the past two hundred years, medicine is not yet an exact science. Doctors still do many things that are known to work without fully understanding how they work. Much of medicine is built on messy approximations that are usually mostly right for most patients but probably aren’t totally right for you.

Turning medicine into an exact science will require transforming it into an information technology—allowing it to benefit from the exponential progress of information technologies. This profound paradigm shift is now well underway, and it involves combining biotechnology with AI and digital simulations. We are already seeing immediate benefits—from drug discovery to disease surveillance and robotic surgery. But the most fundamental benefit of AI–biotech convergence is even more significant.

When medicine relied solely on painstaking laboratory experimentation and human doctors passing their expertise down to the next generation, innovation made plodding, linear progress. But AI can learn from more data than a human doctor ever could and can amass experience from billions of procedures instead of the thousands a human doctor can perform in a career. And since artificial intelligence benefits from exponential improvements to its underlying hardware, as AI plays an ever greater role in medicine, health care will reap the exponential benefits as well. With these tools we’ve already begun finding answers to biochemical problems by digitally searching through every possible option and identifying solutions in hours rather than years.

Perhaps the most important class of problems at present is designing treatments for emerging viral threats. This challenge is like finding which key will open a given virus’s chemical lock—from a pile of keys that could fill a swimming pool. A human researcher using her own knowledge and cognitive skills might be able to identify a few dozen molecules with potential to treat the disease, but the actual number of possibly relevant molecules is generally in the trillions. When these are sifted through, most will obviously be inappropriate and won’t warrant full simulation, but billions of possibilities may warrant a more robust computational examination. At the other extreme, the space of physically possible potential drug molecules has been estimated to contain some 1 million billion billion billion billion billion billion possibilities! However one frames the exact number, AI now lets scientists sort through that gigantic pile to focus on those keys most likely to fit for a given virus.

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Think of the advantages of this kind of exhaustive search. In our current paradigm, once we have a potentially feasible disease-fighting agent, we can organize a few dozen or a few hundred human subjects and then test them in clinical trials over the course of months or years, at a cost of tens or hundreds of millions of dollars. Very often this first option is not ideal: it requires exploration of alternatives, which will also take a few years to test. Not much further progress can be made until those results are available. The US regulatory process involves three main phases of clinical trials, and according to a recent MIT study, only 13.8 percent of candidate drugs make it all the way through to FDA approval. The ultimate result is a process that typically takes a decade to bring a new drug to market, at an average cost estimated at between $1.3 billion and $2.6 billion.

In just the past few years, the pace of AI-assisted breakthroughs has increased noticeably. In 2019 researchers at Flinders University, in Australia, created a “turbocharged” flu vaccine by using a biology simulator to discover substances that activate the human immune system. It digitally generated trillions of chemicals, and the researchers, seeking the ideal formulation, used another simulator to determine whether each of them would be useful as an immune-boosting drug against the virus.

In 2020 a team at MIT used AI to develop a powerful antibiotic that kills some of the most dangerous drug-resistant bacteria in existence. Rather than evaluate just a few types of antibiotics, it analyzed 107 million of them in a matter of hours and returned twenty-three potential candidates, highlighting two that appear to be the most effective. According to University of Pittsburgh drug design researcher Jacob Durrant, “The work really is remarkable. This approach highlights the power of computer-aided drug discovery. It would be impossible to physically test over 100 million compounds for antibiotic activity.” The MIT researchers have since started applying this method to design effective new antibiotics from scratch.

But by far the most important application of AI to medicine in 2020 was the key role it played in designing safe and effective COVID-19 vaccines in record time. On January 11, Chinese authorities released the virus’s genetic sequence. Moderna scientists got to work with powerful machine-learning tools that analyzed what vaccine would work best against it, and just two days later they had created the sequence for its mRNA vaccine. On February 7 the first clinical batch was produced. After preliminary testing, it was sent to the National Institutes of Health on February 24. And on March 16—just 63 days after sequence selection—the first dose went into a trial participant’s arm. Before the pandemic, vaccines typically took five to ten years to develop. Achieving this breakthrough so quickly surely saved millions of lives.

But the war isn’t over. In 2021, with COVID-19 variants looming, researchers at USC developed an innovative AI tool to speed adaptive development of vaccines that may be needed as the virus continues to mutate. Thanks to simulation, candidate vaccines can be designed in less than a minute and digitally validated within one hour. By the time you read this, even more advanced methods will likely be available.

All the applications I’ve described are instances of a much more fundamental challenge in biology: predicting how proteins fold. The DNA instructions in our genome produce sequences of amino acids, which fold up into a protein whose three-dimensional features largely control how the protein actually works. Our bodies are mostly made of proteins, so understanding the relationship between their composition and function is key to developing new medicines and curing disease. Unfortunately, humans have had a fairly low accuracy rate at predicting protein folding, as the complexity involved defies any single easy-to-conceptualize rule. Thus, discoveries still depend on luck and laborious effort, and optimal solutions may remain undiscovered. This has long been one of the main obstacles to achieving new pharmaceutical breakthroughs.

This is where the pattern recognition capabilities of AI offer a profound advantage. In 2018 Alphabet’s DeepMind created a program called AlphaFold, which competed against the leading protein-folding predictors, including both human scientists and earlier software-driven approaches. DeepMind did not use the usual method of drawing on a catalog of protein shapes to be used as models. Like AlphaGo Zero, it dispensed with established human knowledge. AlphaFold placed a prominent first out of ninety-eight competing programs, having accurately predicted twenty-five out of forty-three proteins, whereas the second-place competitor got only three out of forty- three.

Yet the AI predictions still weren’t as accurate as lab experiments, so DeepMind went back to the drawing board and incorporated transformers—the deep-learning technique that powers GPT-3. In 2021 DeepMind publicly released AlphaFold 2, which achieved a truly stunning breakthrough. The AI is now able to achieve nearly experimental-level accuracy for almost any protein it is given. This suddenly expands the number of protein structures available to biologists from over 180,000 to hundreds of millions, and it will soon reach the billions. This will greatly accelerate the pace of biomedical discoveries.

At present, AI drug discovery is a human-guided process— scientists have to identify the problem they are trying to solve, formulate the problem in chemical terms, and set the parameters of the simulation. Over the coming decades, though, AI will gain the capacity to search more creatively. For example, it might identify a problem that human clinicians hadn’t even noticed (e.g., that a particular subset of people with a certain disease don’t respond well to standard treatments) and propose complex and novel therapies.

Meanwhile, AI will scale up to modeling ever larger systems in simulation—from proteins to protein complexes, organelles, cells, tissues, and whole organs. Doing so will enable us to cure diseases whose complexity puts them out of the reach of today’s medicine. For example, the past decade has seen the introduction of many promising cancer treatments, including immunotherapies like CAR-T, BiTEs, and immune checkpoint inhibitors. These have saved thousands of lives, but they frequently still fail because cancers learn to resist them. Often this involves tumors altering their local environment in ways we can’t fully understand with current techniques. When AI can robustly simulate the tumor and its microenvironment, though, we’ll be able to tailor therapies to overcome this resistance.

Likewise, such neurodegenerative diseases as Alzheimer’s and Parkinson’s involve subtle, complex processes that cause misfolded proteins to build up in the brain and inflict harm. Because it’s impossible to study these effects in a living brain, research has been extremely slow and difficult. With AI simulations we’ll be able to understand their root causes and treat patients effectively long before they become debilitated. Those same brain-simulation tools will also let us achieve breakthroughs for mental health disorders, which are expected to affect more than half the US population at some point in their lives. So far doctors have relied on blunt-approach psychiatric drugs like SSRIs and SNRIs, which temporarily adjust chemical imbalances but often have modest benefits, don’t work at all for some patients, and carry long lists of side effects. Once AI gives us a full functional understanding of the brain—the most complex structure in the known universe! —we’ll be able to target many mental health problems at their source.

From The Singularity is Nearer: When We Merge With AI by Ray Kurzweil, to be published on June 25, 2024 by Viking, an imprint of Penguin Publishing Group, a division of Penguin Random House, LLC. Copyright © 2024 by Ray Kurzweil.

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About Ray Kurzweil

Ray Kurzweil is a futurist, inventor and the author of The Singularity is Nearer: When We Merge With AI. He’s based in Boston, Massachusetts.

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