The symptoms and side effects of Covid-19 are scattered across a diagnostic spectrum. Some patients are asymptomatic or experience a mild immune response, while others report significant long-term illnesses, lasting complications, or suffer fatal outcomes. Three researchers from the Georgia Institute of Technology and one from Emory University are trying to help clinicians sort through these factors and spectrum of patient outcomes by equipping healthcare professionals with a new "decision prioritization tool." The team's new artificial intelligence-based tool helps clinicians understand and better predict which adverse effects their Covid-19 patients could experience, based on comorbidities and current side effects -; and, in turn, also helps suggest specific Food and Drug Administration-approved (FDA) drugs that could help treat the disease and improve patient health outcomes. The researcher's latest findings are the focus of a new study published October 21 in Scientific Reports. The team's new methodology, or tool, is called MOATAI-VIR (Mode Of Action proteins & Targeted therapeutic discovery driven by Artificial Intelligence for VIRuses.

The world is facing the COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Likewise, other viruses of the Coronaviridae family were responsible for causing epidemics earlier. To tackle these viruses, there is a lack of approved antiviral drugs. Therefore, we have developed robust computational methods to predict the repurposed drugs using machine learning techniques namely Support Vector Machine, Random Forest, k-Nearest Neighbour, Artificial Neural Network, and Deep Learning. We used the experimentally validated drugs/chemicals with anticorona activity (IC50/EC50) from'DrugRepV' repository.

With Canadian infection rates recently breaking the 1 million mark and deaths topping 23,000 and counting, and the global infection rate reaching 132 million, with nearly 3 million deaths to date, intervention is critical. "By targeting human proteins necessary for the coronavirus lifecycle, we are developing therapeutic strategies applicable to future strains or variants" said Dr. Bo Wang of the Vector Institute. "Unlike high throughput screens, accurate AI predictions aimed at specific human-based, antiviral targets provided us with a risk-adjusted strategy to search for drug-repurposing opportunities using scientifically accurate experiments with high biological relevance." The discovery of capmatinib's antiviral activity is a shining example of the power of collaborative research programs. The Vector Institute team's graph convolutional network (GCN) identified possible human targets relevant to COVID-19, which fed directly into Cyclica's PolypharmDB - a database for finding repurposing opportunities, generated using Cyclica's deep learning approach MatchMaker.

The Digital DNA series explores the role of large-scale genetic testing in science, industry and society. We aim to understand both the benefits and risks of this emerging technology and see what the future may hold. In the finale of our Digital DNA series we talked to Dr Ian Roberts, the Chief Technology Officer of Healx. He describes how Healx uses artificial intelligence (AI) to repurpose already-approved drugs as treatments for rare diseases. IR: Healx are an AI-powered biotech, and we accelerate the discovery and development of treatments for rare diseases.