{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T09:27:20Z","timestamp":1774430840404,"version":"3.50.1"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:p>The high heterogeneity of the disease microenvironment is a critical factor contributing to therapeutic failure and the emergence of drug resistance; however, predicting drug responses with precision at single-cell resolution remains a substantial challenge. Traditional pharmacogenomic studies are constrained by averaged signals at the population level, which frequently obscure rare yet lethal resistant subpopulations. This article reviews a closed-loop strategy that integrates computational pharmacology with cell biology to address this dilemma. First, we explore computational frameworks based on Deep Transfer Learning and Domain Adaptation, such as scDEAL and SCAD. These algorithms can transfer pharmacological knowledge from large-scale cell lines to clinical single-cell data, thereby enabling virtual prediction of cellular drug sensitivity in the absence of experimental labels. Second, based on algorithmic predictions, we elucidate chemotherapy-induced Transcriptional Stress States and their co-evolutionary mechanisms with inflammatory stromal cells, as well as interactions that construct an immunosuppressive barrier protecting residual disease. Finally, we demonstrate the feasibility of reprogramming these specific pathological states using small-molecule drugs (e.g., decitabine, benzofuran derivatives), including the reversal of macrophage polarization imbalance in spinal cord injury and the amelioration of osteogenic differentiation disorders in osteoporosis. This integrated \u201calgorithm prediction\u2013mechanism elucidation\u2013drug intervention\u201d strategy provides a novel paradigm for precision therapy to reverse disease-associated cell fates.<\/jats:p>","DOI":"10.3389\/fphar.2026.1786561","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T06:54:46Z","timestamp":1774421686000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Algorithmically defined therapeutic targets: integrating single-cell transfer learning frameworks with small molecule drugs to reverse disease-associated cell fates"],"prefix":"10.3389","volume":"17","author":[{"given":"Xiaofeng","family":"Ma","sequence":"first","affiliation":[{"name":"Key Laboratory for Space Biosciences & Biotechnology, School of Life Science and Technology, Institute of Special Environmental Biophysics, Research Center of Special Environmental Biomechanics and Medical Engineering, Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology and Equipment, Northwestern Polytechnical University","place":["Xi\u2019an, Shaanxi, China"]},{"name":"Emergency Center, The Second Hospital of Lanzhou University","place":["Lanzhou, Gansu, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuo","family":"Zuo","sequence":"additional","affiliation":[{"name":"Key Laboratory for Space Biosciences & Biotechnology, School of Life Science and Technology, Institute of Special Environmental Biophysics, Research Center of Special Environmental Biomechanics and Medical Engineering, Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology and Equipment, Northwestern Polytechnical University","place":["Xi\u2019an, Shaanxi, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Shi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Gansu Province for Urological Diseases, Institute of Urology, Gansu Urological Clinical Center, The Second Hospital of Lanzhou University","place":["Lanzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulong","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory for Space Biosciences & Biotechnology, School of Life Science and Technology, Institute of Special Environmental Biophysics, Research Center of Special Environmental Biomechanics and Medical Engineering, Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology and Equipment, Northwestern Polytechnical University","place":["Xi\u2019an, Shaanxi, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"6494","DOI":"10.1038\/s41467-022-34277-7","article-title":"Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data","volume":"13","author":"Chen","year":"2022","journal-title":"Nat. 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