Although still preliminary, the machine learning solution has allowed them to identify about 390 potential drugs, that may be able to act on the virus’ therapeutic targets and the infection process. Safety, the four that are yielding better results, and which are already in the trial stage, are choloriquine and hydroxichloriquine, which are already used against malaria, oseltamivir (remdesivir), an antiviral drug, and tocilizumab (Actemra) an immunosupressant for rheumatoid arthritis. “What’s interesting is that the program has precisely recommended chloroquine and hydroxychloroquine, which evidences the reliabillity of the process, and the other drugs that it is recommending may be valid,” says Dopazo.
The countdown to find a treatment
This research project attests to the tremendous biomedical potential of machine learning as scientific tool. Thanks to the progress in this field, smart machines can be trained already to classify datasets with human-like accuracy, if not better. “This type of artificial intelligence can help you detect patterns and relationships very efficiently. It’s like a GPS that guides you as you search and allows you to aim much more accurately when trying to find potential therapeutic targets,” says Dopazo. For the researcher, “the machine doesn’t do anything a human being wouldn’t be able to, but it is very useful because it helps detect cause-effect links without biases, which is what people typically do.”
This way, the artificial intelligence solution can help identify much faster whether any of the drugs in use to treat other diseases can be used against COVID-19. This would imply significant cost savings and, more importantly, shortening the time needed to come up with an effective treatment in just a few months.
Credit: Google News