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• W4386774951 abstract "Understanding the Mechanism of Action (MoA) of a compound is an often challenging but equally crucial aspect of drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim to predict direct drug targets, or attempt to understand modulated downstream pathways or signalling proteins. Such methods usually require extensive coding experience and results are often optimised for further computational processing, making them difficult for wet-lab scientists to perform, interpret and draw hypotheses from.To address this issue, we in this work present MAVEN (Mechanism of Action Visualisation and Enrichment), an R/Shiny app which allows for GUI-based prediction of drug targets based on chemical structure, combined with causal reasoning based on causal protein-protein interactions and transcriptomic perturbation signatures. The app computes a systems-level view of the mechanism of action of the input compound. This is visualised as a sub-network linking predicted or known targets to modulated transcription factors via inferred signalling proteins. The tool includes a selection of MSigDB gene set collections to perform pathway enrichment on the resulting network, and also allows for custom gene sets to be uploaded by the researcher. MAVEN is hence a user-friendly, flexible tool for researchers without extensive bioinformatics or cheminformatics knowledge to generate interpretable hypotheses of compound Mechanism of Action.MAVEN is available as a fully open-source tool at https://github.com/laylagerami/MAVEN with options to install in a Docker or Singularity container. Full documentation, including a tutorial on example data, is available at https://laylagerami.github.io/MAVEN ." @default.
• W4386774951 created "2023-09-16" @default.
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• W4386774951 date "2023-09-15" @default.
• W4386774951 modified "2023-10-14" @default.
• W4386774951 title "MAVEN: compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny" @default.
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• W4386774951 doi "https://doi.org/10.1186/s12859-023-05416-8" @default.
• W4386774951 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37715141" @default.
• W4386774951 hasPublicationYear "2023" @default.
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