Leveraging AI to hunt for potential treatments: a COVID-19 example

1 May, 2020

Artificial Intelligence and Machine Learning get a lot of attention these days. Machine Learning (ML) is a mathematical technique that uses empirical data to generate an algorithm that can predict or make decisions on new data. But the old adage of “garbage-in, garbage-out” especially applies in ML, so it is important to understand the data that is used to generate these models, and thoroughly question the answers the models give us. In general, ML should be used to narrow decision making, or reduce noise in data so that the task of analysis is lessened. In some well-determined cases, decisions can be made automatically (i.e., facial recognition). As a science and technology company in the biomedical space, BioTeam is interested in better understanding how ML can be used to speed drug discovery. Here, we show an extremely relevant ML exercise we have been working on for the last couple of weeks that is a naive attempt to identify a drug that could be active against SARS-CoV-2 (COV).

Researchers around the world are racing to find treatments and vaccines to eradicate COVID-19. Researchers can not leave any stone unturned. How do you search through mountains of compounds to find effective treatments? One answer is using ML to search traditional and non-traditional places for a treatment. Could there be an approved drug already out there that could be used as an “off-label” for treatment COVID-19? While we are not offering scientific insight into this problem here, we do demonstrate how biomedical research organizations and pharmaceutical companies might be able to speed the discovery of treatment options using ML.

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