The science and practice of medicine has been around for much of recorded human history. Even today, doctors still swear an oath that dates back to ancient Greece, containing many of the ethical obligations we still expect our physicians to adhere to. It is one of the most necessary and universal fields of human study.
Despite the importance of medicine, though, true breakthroughs don't come easily. In fact, most medical professionals will only see a few within their lifetime. Developments such as the first medical x-ray, penicillin, stem cell therapy - true game changers that advance the cause of medical care don't happen often.
That's especially true when it comes to the development of medications. It takes a great deal of research and testing to find compounds that have medicinal benefits. Armies of scientists armed with microplate readers to measure absorbance, centrifuges for sample separation, and hematology analyzers to test compound efficacy make up just the beginnings of the long and labor-intensive process. It's why regulators tend to approve around 22 new drugs per year for public use, leaving many afflicted patients waiting for cures that may come too late.
Now, however, some recent advances in AI technology are promising to speed that process up. It could be the beginnings of a new medical technology breakthrough on the same order of magnitude as the ones mentioned earlier. Here's what's going on.
Speeding up Molecule Screening
One of the reasons that it takes so long to develop new drug therapies, even for diseases that have been around for decades, is that much of the process relies on humans screening different molecule types to find ones likely to have an effect on the disease in question. Much of that work calls for lengthy chemical property analysis, followed by structured experimentation. On average, all of that work takes between three and six years to complete.
Recently, researchers have begun to adapt next-generation AI implementations for molecule screening that could cut that time down significantly. In one test, a startup called Insilico Medicine matched its' AI platform up against the already-completed work of human researchers seeking treatment options for fibrosis. It had taken them 8 years to come up with viable candidate molecules. It took the AI just 21 days. Although further refinements are required to put the AI on par with the human researchers in terms of result quality (the AI candidates performed a bit worse in treating fibrosis), the results were overwhelmingly positive.
Predicting Side Effects and Toxicity
Another major time-consuming hurdle that drug developers face is in trying to detect adverse side effects or toxicity in their new compounds. It's difficult because such effects don't always surface in clinical trials. Some take years to show up, long after scores of patients have already suffered from them. To avoid those outcomes, pharmaceutical firms take lots of time to study similar compounds that have already have reams of human interaction data, looking for patterns that could indicate a problem.
It's yet another part of the process that AI is proving adept at. AI systems can analyze vast amounts of data about known compounds to generate predictions about how a new molecule may behave. They can also model interactions between a new compound and different physical and chemical environments. That can provide clues to how a new drug might affect different parts of a human body. Best of all, AI can accomplish those tasks with more accuracy and in a fraction of the time it would take a human research team.
AI-Developed Drugs are Around the Corner
Even at this early stage of the development of drug discovery AI systems, there's every reason to believe that AI-developed drugs will be on the market in the very near future. In fact, there's already an AI-designed drug intended to treat obsessive-compulsive disorder (OCD) entering human trials in Japan. If successful, it will then proceed to worldwide testing and eventual regulatory approval processes in multiple countries.
It's worth noting that the drug in question took a mere 12 months for the AI to create, which would represent a revolution in the way we develop new disease treatments. With that as a baseline, it's easy to foresee drug development and testing cycles in the future reduced to weeks, not years. It's also easy to predict the advent of personalized drug development, with AI selecting and creating individualized treatments using patient physiological and genetic data. Such outcomes would render the medical field unrecognizable compared to today - and could create a disease-free future and a new human renaissance like nothing that's come before it.
