A new deep-learning model that can predict how human genes and medicines will interact has identified at least 10 compounds that may hold promise as treatments for COVID-19.
All but two of the drugs are still considered investigational and are being tested for effectiveness against hepatitis C, fungal disease, cancer and heart disease. The list also includes the approved drugs cyclosporine, an immunosuppressant that prevents transplant organ rejection, and anidulafungin, an antifungal agent.
The discovery was made by computer scientists, meaning much more work needs to be done before any of these medications would be confirmed as safe and effective treatments for people infected with SARS-CoV-2. But by using artificial intelligence to arrive at these options, the scientists have saved pharmaceutical and clinical researchers the time and money it would take to search for potential COVID-19 drugs on a piecemeal basis.
“When no one has any information on a new disease, this model shows how artificial intelligence can help solve the problem of how to consider a potential treatment,” said senior author Ping Zhang, assistant professor of computer science and engineering and biomedical informatics at The Ohio State University.
The researchers noted in the paper that a few of the repurposing candidates the model generated have already been studied for their potential use in COVID-19 patients.
“Great minds think alike—some lead compounds identified by machine intelligence coincide with later discoveries by human intelligence,” Zhang said.
The research is published today (Feb. 1) in Nature Machine Intelligence.
Zhang and colleagues had completed the model’s design in May 2020, just as the first papers detailing how COVID-19 patients’ genes responded to