Machine learning prediction of side effects for drugs in clinical trials
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2022-12-19
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Early and accurate detection of side effects is critical for the clinical success of drugs under development.
Here, we aim to predict unknown side effects for drugs with a small number of side effects identified in randomized
controlled clinical trials. Our machine learning framework, the geometric self-expressive model
(GSEM), learns globally optimal self-representations for drugs and side effects from pharmacological graph
networks. We show the usefulness of the GSEM on 505 therapeutically diverse drugs and 904 side effects
from multiple human physiological systems. Here, we also show a data integration strategy that could be
adopted to improve the ability of side effect prediction models to identify unknown side effects that might
only appear after the drug enters the market.