Using ORNL's Summit supercomputer, scientists can comb through millions of medical journal articles looking for possible connections among FDA-approved drug therapies and known COVID-19 symptoms. Scientists have tapped the immense power of the Summit supercomputer at Oak Ridge National Laboratory to comb through millions of medical journal articles to identify potential vaccines, drugs, and effective measures that could suppress or stop the spread of COVID-19. A team comprising researchers from ORNL and Georgia Tech are using artificial intelligence methods designed to unearth relevant information from about 18 million available research documents. They looked for connections among 84 billion concepts and cross-referenced keywords associated with COVID-19 -- such as high fever, dry cough, and shortness of breath -- with existing medical solutions. "Our goal is to assist doctors' and researchers' ability to identify information about drug therapies that are already approved by the U.S. Federal Drug Administration," said ORNL's Ramakrishnan "Ramki" Kannan.

The appearance of a new dangerous and contagious disease requires the development of a drug therapy faster than what is foreseen by usual mechanisms. Many drug therapy developments consist in investigating through different clinical trials the effects of different specific drug combinations by delivering it into a test group of ill patients, meanwhile a placebo treatment is delivered to the remaining ill patients, known as the control group. We compare the above technique to a new technique in which all patients receive a different and reasonable combination of drugs and use this outcome to feed a Neural Network. By averaging out fluctuations and recognizing different patient features, the Neural Network learns the pattern that connects the patients initial state to the outcome of the treatments and therefore can predict the best drug therapy better than the above method. In contrast to many available works, we do not study any detail of drugs composition nor interaction, but instead pose and solve the problem from a phenomenological point of view, which allows us to compare both methods. Although the conclusion is reached through mathematical modeling and is stable upon any reasonable model, this is a proof-of-concept that should be studied within other expertises before confronting a real scenario. All calculations, tools and scripts have been made open source for the community to test, modify or expand it. Finally it should be mentioned that, although the results presented here are in the context of a new disease in medical sciences, these are useful for any field that requires a experimental technique with a control group.

Digital technologies are becoming ubiquitous, effective, and cost efficient, but are underutilized in medicine. These technologies -- like wearable health monitors, sensors, and even ingestible devices that can measure everything from how many steps you take, to blood pressure, and how a drug interacts with your body once ingested -- have the potential to disrupt every aspect of health care, including high-stakes, high-cost drug development. Specifically, these devices can revolutionize the antiquated process of developing new drug therapies and can vastly improve how we collect, measure, and assess health data so that we can offer new treatments to patients without wasting valuable time and limited resources. Clinical trials are designed to evaluate whether a new drug is safe and effective while protecting volunteer patients participating in the trials from risk. All good intentions, but some of today's research processes date back to 1946. New and proven digital technologies can make drug development smarter, better, and faster.