ReviewRSC chemical biology2022
Computational analyses of mechanism of action (MoA): data, methods and integration.
Review in RSC chemical biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed.
- Deciphering the Mechanisms of Statin-Ezetimibe Drug Combinations Using Boolean Logical Modeling and Transcriptomic Data.CPT: pharmacometrics & systems pharmacology · 2026Article
- DR.DEGMON: self-explainable deep neural network for drug-induced cell viability prediction incorporating differentially expressed genes and gene ontology.BMC medical genomics · 2026Article
- Network-based exploration of 4-(phenylsulfonyl)morpholine molecules for metastatic triple-negative breast cancer suppression.PLoS computational biology · 2026Article
- Harnessing transcriptomics for discovery of natural products to overcome acquired cancer resistance.Archives of pharmacal research · 2026Review
- DeepTargetClass: a web-based platform for predicting protein target classes of small molecules.Journal of computer-aided molecular design · 2025Article
- Prediction of cellular morphology changes under perturbations with a transcriptome-guided diffusion model.Nature communications · 2025Article
- Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks.Communications biology · 2025Article
- Cell Painting: a decade of discovery and innovation in cellular imaging.Nature methods · 2025Review
- Semisupervised Contrastive Learning for Bioactivity Prediction Using Cell Painting Image Data.Journal of chemical information and modeling · 2025Article
- A comprehensive review on computational metabolomics: Advancing multiscale analysis throughComputational and structural biotechnology journal · 2025Review
- Genetic evidence informs the direction of therapeutic modulation in drug development.npj drug discovery · 2025Article
- Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024Review
- Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics.Cell genomics · 2024Article
- A genome-scale deep learning model to predict gene expression changes of genetic perturbations from multiplex biological networks.Briefings in bioinformatics · 2024Article
- A Decade in a Systematic Review: The Evolution and Impact of Cell Painting.bioRxiv : the preprint server for biology · 2024Article
- Article
- Unleashing the potential of cell painting assays for compound activities and hazards prediction.Frontiers in toxicology · 2024Review
- MAVEN: compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny.BMC bioinformatics · 2023Article
- Evaluating the utility of brightfield image data for mechanism of action prediction.PLoS computational biology · 2023Article
- Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data.Journal of cheminformatics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.