ArticleNature biotechnology2020
Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker.
Article in Nature biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 68 papers.
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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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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.
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Who cites it
68 citing papers in PubMed.
- Bioactivity-driven prediction of antibacterial synergy using machine learning models.PLoS computational biology · 2026Article
- CBInformax: bioactivity-aware self-supervised molecular representation learning for molecular property and drug-drug interaction prediction.Molecular diversity · 2026Article
- Exploration of the protein and pharmacological landscape of monkeypox virus treatment: from entry point to end point.Molecular diversity · 2026Review
- Phenotypic AI-based design of cell-specific small molecule cytotoxics.Communications chemistry · 2026Article
- A Scalable Sign-Aware Multi-Omics Knowledge Graph Foundation Model for Mechanistic Drug Action and Clinical Response Predictions.bioRxiv : the preprint server for biology · 2026Article
- A network-based map of the chemical exposome connects molecular interactions to public health.Nature communications · 2026Article
- A comprehensive review of cluster methods for drug-drug interaction network.Quantitative biology (Beijing, China) · 2026Review
- Medea: An omics AI agent for therapeutic discovery.bioRxiv : the preprint server for biology · 2026Article
- Article
- SynergyGraph: predicting cell line specific drug combination synergy scores using knowledge graph representation and hypergraph modeling.Scientific reports · 2025Article
- Unravelling the multifaceted actions of neurosteroids: Machine learning and in vitro screening for novel target discovery.British journal of pharmacology · 2025Article
- Integration of diverse bioactivity data into the Chemical Checker compound universe.Nature protocols · 2025Review
- Artificial intelligence coupled to pharmacometrics modelling to tailor malaria and tuberculosis treatment in Africa.Nature communications · 2025Article
- Advances in Computational Drug Repurposing, Driver Genes, and Therapeutics in Lung Adenocarcinoma.Biomolecules · 2025Review
- Chemical Language Model Linker: Blending Text and Molecules with Modular Adapters.Journal of chemical information and modeling · 2025Article
- A bottom-up approach to find lead compounds in expansive chemical spaces.Communications chemistry · 2025Article
- Computational drug repurposing: approaches, evaluation of in silico resources and case studies.Nature reviews. Drug discovery · 2025Review
- Article
- InertDB as a generative AI-expanded resource of biologically inactive small molecules from PubChem.Journal of cheminformatics · 2025Article
- Natural product virtual-interact-phenotypic target characterization: A novel approach demonstrated with Salvia miltiorrhiza extract.Journal of pharmaceutical analysis · 2025Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods.
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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.