ArticleNature machine intelligence2021
A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing.
Article in Nature machine intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
109 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information.Progress in biophysics and molecular biology · 2023Pooled it
- AET5: A transcriptome-guided molecular generation framework with contrastive self-supervised learning.PLoS computational biology · 2026Article
- Drug target prediction from perturbation transcriptomics via a biological function-guided hypergraph siamese network.Bioinformatics (Oxford, England) · 2026Article
- Topology-Aware Deep Learning on Higher-Order Structures for Drug Response Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Enhancing cross-context generalization in drug perturbation prediction with a multimodal conditional diffusion framework.Bioinformatics (Oxford, England) · 2026Article
- Advances in high-throughput drug screening based on pharmacotranscriptomics.Journal of advanced research · 2026Review
- ExPO: an exposure-conditioned neural operator for L1000 signature prediction.Journal of cheminformatics · 2026Article
- The architecture of computational antiviralism: a multi-scale framework from molecular targeting to viral ecosystem engineering.Molecular diversity · 2026Review
- Combinatorial prediction of therapeutic perturbations using causally inspired neural networks.Nature biomedical engineering · 2026Article
- DeepICER: A deep learning framework for predicting compound-induced gene expression profiles.Acta pharmaceutica Sinica. B · 2026Article
- Article
- DeepDrugDiscovery identifies blood-brain barrier permeable autophagy enhancers for Alzheimer's disease.Nature biomedical engineering · 2026Article
- Predicting condition-aware drug-induced transcriptional responses via a latent diffusion model.Bioinformatics (Oxford, England) · 2026Article
- scLong: a billion-parameter foundation model for capturing long-range gene context in single-cell transcriptomics.Nature communications · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Omics-based large language models: A new engine for drug discovery innovation.Acta pharmaceutica Sinica. B · 2026Review
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- AI-drivenPeerJ · 2026Article
- Integrating artificial intelligence across the cancer drug discovery pipeline using a design-test-refine workflow.Frontiers in oncology · 2026Review
- DVFGCDR: a dual-view fusion graph neural network for cancer drug response prediction.NAR cancer · 2025Article
49 more citing papers are in PubMed but not listed here.
Corrections and comments
- Update of
Authors and funding
5 authors.
Funding
Abstract
Phenotype-based compound screening has advantages over target-based drug discovery, but is unscalable and lacks understanding of mechanism. Chemical-induced gene expression profile provides a mechanistic signature of phenotypic response. However, the use of such data is limited by their sparseness, unreliability, and relatively low throughput. Few methods can perform phenotype-based
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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.