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. 2022 Feb 19;23(1):76.
doi: 10.1186/s12859-022-04590-5.

DrugShot: querying biomedical search terms to retrieve prioritized lists of small molecules

Eryk Kropiwnicki  1 , Alexander Lachmann  1 , Daniel J B Clarke  1 , Zhuorui Xie  1 , Kathleen M Jagodnik  1 , Avi Ma'ayan  2
Affiliations

DrugShot: querying biomedical search terms to retrieve prioritized lists of small molecules

Eryk Kropiwnicki et al. BMC Bioinformatics. .

Abstract

Background: PubMed contains millions of abstracts that co-mention terms that describe drugs with other biomedical terms such as genes or diseases. Unique opportunities exist for leveraging these co-mentions by integrating them with other drug-drug similarity resources such as the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 signatures to develop novel hypotheses.

Results: DrugShot is a web-based server application and an Appyter that enables users to enter any biomedical search term into a simple input form to receive ranked lists of drugs and other small molecules based on their relevance to the search term. To produce ranked lists of small molecules, DrugShot cross-references returned PubMed identifiers (PMIDs) with DrugRIF or AutoRIF, which are curated resources of drug-PMID associations, to produce an associated small molecule list where each small molecule is ranked according to total co-mentions with the search term from shared PubMed IDs. Additionally, using two types of drug-drug similarity matrices, lists of small molecules are predicted to be associated with the search term. Such predictions are based on literature co-mentions and signature similarity from LINCS L1000 drug-induced gene expression profiles.

Conclusions: DrugShot prioritizes drugs and small molecules associated with biomedical search terms. In addition to listing known associations, DrugShot predicts additional drugs and small molecules related to any search term. Hence, DrugShot can be used to prioritize drugs and preclinical compounds for drug repurposing and suggest indications and adverse events for preclinical compounds. DrugShot is freely and openly available at: https://maayanlab.cloud/drugshot and https://appyters.maayanlab.cloud/#/DrugShot .

Keywords: Drug repurposing; Machine learning; Search engine; Text mining; Transcriptomics.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
A graphical schema of the DrugShot workflow
Fig. 2
Fig. 2
DrugShot web application user interface. A Input form section for querying a biomedical search term of interest. B Scatter plot of all publications that mention both the drug and the search terms against the normalized values. C Tables providing a ranked list of associated drugs from DrugRIF (left), and predictions based on signature similarity (right)
Fig. 3
Fig. 3
The DrugShot Appyter. A Input form where the user can select a biomedical term of interest, unweighted drug set size, the database of drug-PMID associations, and the method to rank the small molecules from the unweighted drug set. Additionally, the user can select which drug-drug similarity matrix to use to make predictions. B The executed notebook with options for download, toggling code, and running the notebook locally. Each of the elements in the table of contents is interactive for easy navigation of the Appyter notebook.
Fig. 4
Fig. 4
Violin plots of AUROC distributions for each collection of search terms for each prediction matrix compared with random shuffles of the prediction matrix. AUROCs for each term were determined based on the rankings of the unweighted drug set created from AutoRIF in each prediction matrix. A AutoRIF literature co-mentions prediction matrix. B DrugRIF literature co-mentions prediction matrix. C Signature similarity prediction matrix
Fig. 5
Fig. 5
Violin plots of average precision score distributions for each collection of search terms for each prediction matrix compared with random shuffles of the prediction matrix. Average precision scores for each term were determined based on the rankings of the unweighted drug set created from AutoRIF in each prediction matrix. A AutoRIF literature co-mentions prediction matrix. B DrugRIF literature co-mentions prediction matrix. C Signature similarity prediction matrix
Fig. 6
Fig. 6
Violin plots of AUROC distributions for each collection of search terms for each prediction matrix compared with random shuffles of the prediction matrix. AUROCs for each term were determined based on the rankings of the unweighted drug set created from DrugRIF in each prediction matrix. A AutoRIF literature co-mentions prediction matrix. B DrugRIF literature co-mentions prediction matrix. C Signature similarity prediction matrix
Fig. 7
Fig. 7
Violin plots of average precision score distributions for each collection of search terms for each prediction matrix compared with random shuffles of the prediction matrix. Average precision scores for each term were determined based on the rankings of the unweighted drug set created from DrugRIF in each prediction matrix. A AutoRIF literature co-mentions prediction matrix. B DrugRIF literature co-mentions prediction matrix. C Signature similarity prediction matrix
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