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As a result, models that perform well in perturbation-response prediction may still learn latent spaces dominated by context-associated structure rather than transferable drug-associated signal. We developed DECANT to learn mechanism-aligned perturbation representations that remain stable across context shifts while preserving response fidelity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>DECANT represents each perturbation as a matched treated\u2013control cell set and separates a context-suppressed, mechanism-aligned perturbation representation from context-dependent response information. The resulting mechanism-aligned perturbation space is shaped to support drug-level retrieval and biological interpretation. Under a fixed drug-level unseen-compound benchmark, DECANT achieved the strongest overall response-difference profile among adapted published perturbation models and strong pseudo-bulk baselines across gene- and program-level metrics. Beyond prediction, DECANT produced embeddings that remained stable across changes in dose, cell line, and treatment time, recovered drug neighborhoods enriched for shared mechanism-family annotations, and linked these neighborhoods to interpretable downstream consequence programs. Ablation analyses showed that mechanism\u2013context decoupling provided the main signal-separation backbone, whereas retrieval-oriented shaping was critical for organizing local representation-space geometry. These results support DECANT as a framework for learning context-robust, mechanism-aligned perturbation representations from single-cell transcriptional responses, providing a basis for mechanism-aligned perturbation analysis and representation-based compound prioritization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The DECANT web server is publicly available at http:\/\/bliulab.net\/DECANT. All source code and analysis scripts are available at https:\/\/github.com\/bliulab\/DECANT and archived on Zenodo at https:\/\/doi.org\/10.5281\/zenodo.21216567.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag662","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T11:28:20Z","timestamp":1788521300000},"source":"Crossref","is-referenced-by-count":0,"title":["DECANT: decoupling mechanism from context in single-cell drug perturbation representation"],"prefix":"10.1093","volume":"42","author":[{"given":"Ren","family":"Qi","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology , Beijing 100081,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjie","family":"Teng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology , Beijing 100081,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology , Beijing 100081,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Cheng","sequence":"additional","affiliation":[{"name":"Zhongguancun Academy , Beijing 100094,","place":["China"]},{"name":"School of Artificial Intelligence, Jilin University , Jilin 130012,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexey K","family":"Shaytan","sequence":"additional","affiliation":[{"name":"Department of Biology, Lomonosov Moscow State University , Moscow 119991, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3685-9469","authenticated-orcid":false,"given":"Bin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology , Beijing 100081,","place":["China"]},{"name":"Zhongguancun Academy , Beijing 100094,","place":["China"]},{"name":"SMBU-MSU-BIT Joint Laboratory on Bioinformatics and Engineering Biology, Shenzhen MSU-BIT University , Shenzhen 518172,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"key":"2026091810512945800_btag662-B1","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"WIREs Comp Stats"},{"key":"2026091810512945800_btag662-B2","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/s41576-020-00292-x","article-title":"Deciphering cell\u2013cell interactions and communication from gene expression","volume":"22","author":"Armingol","year":"2021","journal-title":"Nat Rev Genet"},{"key":"2026091810512945800_btag662-B3","author":"Ba","year":"2016"},{"key":"2026091810512945800_btag662-B4","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"2026091810512945800_btag662-B5","doi-asserted-by":"publisher","first-page":"2887","DOI":"10.1021\/jm9602928","article-title":"The properties of known drugs. 1. 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