Communications of the ACM
Deep Learning Helps Predict Drug Combinations to Fight COVID-19
Two new drug combinations were found using the new approach: remdesivir and reserpine, as well as remdesivir and IQ-1S.
Credit: MIT Computer Science and Artificial Intelligence Lab
A team led by researchers at the Massachusetts Institute of Technology (MIT) used deep learning to identify drug combinations that can fight COVID-19 by modeling interactions between drugs and known biological targets related to the virus.
The approach identifyied two new drug combinations: remdesivir and reserpine, and remdesivir and IQ-1S.
Said MIT's Wengong Jin, "By modeling interactions between drugs and biological targets, we can significantly decrease the dependence on combination synergy data. In contrast to previous approaches using drug-target interaction as fixed descriptors, our method learns to predict drug-target interaction from molecular structures. This is advantageous since a large proportion of compounds have incomplete drug-target interaction information."
The model is not limited to a single strain of SARS-CoV-2, and the researchers applied their approach to HIV and pancreatic cancer as well.
From MIT News
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA
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