Prediction of drug adverse events using deep learning in pharmaceutical discovery

Abstract Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-intensive, expert-dependent, time-consuming and cost-ineffective. Moreover, these methods only focus on detecting the association between drug...

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Bibliographic Details
Published in:Briefings in bioinformatics 2021-03, Vol.22 (2), p.1884-1901
Main Authors: Lee, Chun Yen, Chen, Yi-Ping Phoebe
Format: Article
Language:English
Subjects:
Classification
Deep learning
Drug interaction
Drug interactions
Drugs
Learning algorithms
Machine learning
Side effects
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Summary:Abstract Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-intensive, expert-dependent, time-consuming and cost-ineffective. Moreover, these methods only focus on detecting the association between drugs and their side effects or classifying drug–drug interaction. Motivated by technological advancements and the availability of big data, we provide a review on the detection and classification of side effects using deep learning approaches. It is shown that the effective integration of heterogeneous, multidimensional drug data sources, together with the innovative deployment of deep learning approaches, helps reduce or prevent the occurrence of adverse drug reactions (ADRs). Deep learning approaches can also be exploited to find replacements for drugs which have side effects or help to diversify the utilization of drugs through drug repurposing.
ISSN:1477-4054
1467-5463
1477-4054
DOI:10.1093/bib/bbaa040