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IIT Roorkee scientists predict FDA-approved commercial drugs through AI to tackle Covid-19

The Machine Learning (ML) model based on the naive Bayes algorithm was used to predict the drugs that could be used for the treatment of Covid-19, according to IIT Roorkee statement.

Updated on: Nov 13, 2020, 16:13:14 IST
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Amid the rapid transmission of cases of Covid-19, a group of scientists from IIT Roorkee has leveraged Artificial Intelligence (AI) to predict 10 FDA (Food and Drug Administration) approved commercial drugs available for the treatment of the disease.

The study, led by Prof Soumitra Satapathi shows that around ten FDA approved commercial drugs can be used for repurposing. (Bloomberg)
The study, led by Prof Soumitra Satapathi shows that around ten FDA approved commercial drugs can be used for repurposing. (Bloomberg)

The Machine Learning (ML) model based on the naive Bayes algorithm was used to predict the drugs that could be used for the treatment of Covid-19, according to IIT Roorkee statement.

The study, led by Prof Soumitra Satapathi shows that around ten FDA approved commercial drugs can be used for repurposing.

Among all, it has also been found that the antiretroviral drug Amprenavir would probably be the most effective drug based on the selected criterions.

The study has been published in the prestigious journal PLOS One. The other co-authors of the study include Sovesh Mohapatra, Prathul Nath, Manisha Chatterjee, Neeladrisingha Das, Deepjyoti Kalita and Partha Roy.

According to IIT Roorkee statement, the complexity in drug design and protocols regarding clinical trials often inhibit the process of developing various new drug combinations for the disease.

Many of the predicted drugs are already in use for the treatment of Covid-19, and some are in trials, IIT Roorkee said.

“Given the need of the present time, the present approach will save a lot of resources and time for synthesizing novel drugs and thus will be useful for a vast majority of the medical research community,” Prof. Satapathi added.

 
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