Contexts and contradictions: a roadmap for computational drug repurposing with knowledge inference

Abstract The cost of drug development continues to rise and may be prohibitive in cases of unmet clinical need, particularly for rare diseases. Artificial intelligence-based methods are promising in their potential to discover new treatment options. The task of drug repurposing hypothesis generation...

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Bibliographic Details
Published in:Briefings in bioinformatics 2022-07, Vol.23 (4)
Main Authors: Sosa, Daniel N, Altman, Russ B
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer applications
Context
Drug development
Drug Repositioning
Inference
Knowledge
Knowledge representation
Pharmacodynamics
Pharmacokinetics
Pharmacology
Phenotypes
Proteins
Rare diseases
Review
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Summary:Abstract The cost of drug development continues to rise and may be prohibitive in cases of unmet clinical need, particularly for rare diseases. Artificial intelligence-based methods are promising in their potential to discover new treatment options. The task of drug repurposing hypothesis generation is well-posed as a link prediction problem in a knowledge graph (KG) of interacting of drugs, proteins, genes and disease phenotypes. KGs derived from biomedical literature are semantically rich and up-to-date representations of scientific knowledge. Inference methods on scientific KGs can be confounded by unspecified contexts and contradictions. Extracting context enables incorporation of relevant pharmacokinetic and pharmacodynamic detail, such as tissue specificity of interactions. Contradictions in biomedical KGs may arise when contexts are omitted or due to contradicting research claims. In this review, we describe challenges to creating literature-scale representations of pharmacological knowledge and survey current approaches toward incorporating context and resolving contradictions.
ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbac268