ArticleBioinformatics (Oxford, England)2021
Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- DrugPTM-Bench: A Large-Scale Dataset for Predictive Modeling of Drug-Induced Cell Type-Specific Protein Post-Translational Modifications.bioRxiv : the preprint server for biology · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- A genotype-to-drug diffusion model for generation of tailored anti-cancer small molecules.Nature communications · 2025Article
- Multi-Omic Analysis of Glutamate Excitotoxicity in Primary Neuronal Cultures.Journal of neurochemistry · 2025Article
- Integrated transcriptomics- and structure-based drug repositioning identifies drugs with proteasome inhibitor properties.Scientific reports · 2024Article
- Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes.Annual review of biomedical data science · 2024Review
- MolPLA: a molecular pretraining framework for learning cores, R-groups and their linker joints.Bioinformatics (Oxford, England) · 2024Article
- Factor-specific generative pattern from large-scale drug-induced gene expression profile.Scientific reports · 2023Article
- Apoptosis mechanisms induced by 15d-PMJFrontiers in pharmacology · 2023Article
- Application of perturbation gene expression profiles in drug discovery-From mechanism of action to quantitative modelling.Frontiers in systems biology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
motivationIdentifying mechanism of actions (MoA) of novel compounds is crucial in drug discovery. Careful understanding of MoA can avoid potential side effects of drug candidates. Efforts have been made to identify MoA using the transcriptomic signatures induced by compounds. However, these approaches fail to reveal MoAs in the absence of actual compound signatures.
resultsWe present MoAble, which predicts MoAs without requiring compound signatures. We train a deep learning-based coembedding model to map compound signatures and compound structure into the same embedding space. The model generates low-dimensional compound signature representation from the compound structures. To predict MoAs, pathway enrichment analysis is performed based on the connectivity between embedding vectors of compounds and those of genetic perturbation. Results show that MoAble is comparable to the methods that use actual compound signatures. We demonstrate that MoAble can be used to reveal MoAs of novel compounds without measuring compound signatures with the same prediction accuracy as that with measuring them. AVAILABILITY AND IMPLEMENTATION: MoAble is available at https://github.com/dmis-lab/moable. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.