As pharmaceutical and clinical research organizations around the world race to discover and inoculate the global population with a vaccine for COVID-19, Artificial Intelligence (AI) will play a key role in ensuring that trustworthy, relevant, and timely drug-discovery data can be used to understand and eradicate this global threat.
Drug discovery has traditionally been a slow process, but today AI is greatly shortening the time that such life-saving pharmacological research takes—from years to weeks in some cases. An important example: clinical pharmacologists are successfully using the power of AI to detect and interpret the features of cancer molecules in order to make predictions for new drugs that will successfully target those molecules, leading to the most effective treatments against this killer disease in decades.
To reach this stage in cancer treatment, it took a concerted effort to implement the following steps:
Since AI is successfully predicting what might work best for treating a cancer patient—and in far less time than ever before—the world is desperately hopeful that clinical pharmacologists will be able to use the same techniques to solve other really big problems, like stopping global pandemics with an effective vaccine.
Researchers have become adept at using the power of machine learning (ML) for automating clinical challenges—like predicting, anticipating, and proactively eliminating the inefficiencies and process breakdowns that often occur on the way to starting clinical trials and advising on best courses of action. This is accelerating the pathway to clinical trials and will lead to a quicker vaccine.
It is most likely that a combination of drugs will eventually be used to defeat coronavirus, requiring analysis of not only millions of possible drug pairs, but also billions of triple-drug combinations stemming from over 4,000 approved drugs on the market today. AI-driven analytics is the strongest tool we have to surmount this challenge, but this would ultimately fail without access to large, high-quality, clean, and trustworthy data sets.
Unfortunately, much of the data that is required by the AI models to accelerate coronavirus research to the clinical trial stage is tied up in silos across individual big pharmaceutical companies. Or, it’s buried deep inside the intellectual property within laboratory, university, and disjointed healthcare organization databases all over the world.
As in cancer research, the urgent need is to unify and integrate all of these disparate data sources together in one place. This will enable clinical pharmacologists to use the novel ML techniques perfected on cancer treatment to generate new drugs to attack and vanquish COVID-19.
An inspiring, real-world example of leveraging AI to gather and share clinical research data across healthcare ecosystems for maximum clinical value, speed, and efficiency is the Covid-19 Open AI Consortium (COAI).
