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However, existing transcriptome-guided generative approaches remain limited in biological specificity, transcriptomic coverage, and evaluation across diverse perturbation contexts. Here, we propose DEG2MOL, a biologically grounded conditioning framework that encodes differentially expressed gene profiles through a Gene Ontology (GO)-informed encoder and maps this representation into the latent space of a frozen, pre-trained scaffold-aware VAE via conditional latent flow matching. When benchmarked against five baseline models, DEG2MOL ranked first by rank-sum aggregation of six metrics in both the random- and scaffold-split evaluations. For two representative compound conditions, GO-informed encoder activations were accompanied by enrichment of the corresponding target-engaging pharmacophores among the generated molecules. In docking case studies of two compound\u2013target pairs, generated molecules showed favorable predicted docking scores and reference-associated contacts. DEG2MOL also generated molecules from shRNA knockdown, CRISPR knockout, and Perturb-seq profiles without additional training, indicating transfer across the perturbation types and profiling platforms evaluated. At the transcriptional level, an in silico gene-expression analysis showed that the predicted signatures of generated molecules were more similar to those of target-matched inhibitors than to those of unrelated inhibitors. The data and code are available at https:\/\/github.com\/KU-MedAI\/DEG2MOL.<\/jats:p>","DOI":"10.1021\/acsomega.6c07302","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T06:01:44Z","timestamp":1790575304000},"page":"61631-61645","source":"Crossref","is-referenced-by-count":0,"title":["DEG2MOL: Conditional Latent Flow Matching for Transcriptome-Guided\n                    <i>de novo<\/i>\n                    Drug Design"],"prefix":"10.1021","volume":"11","author":[{"given":"Songhyeon","family":"Kim","sequence":"first","affiliation":[{"name":"Korea University College of Medicine , , 73 Goryeodae-ro , ,","place":["Seoul, Republic of Korea, 02841"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wootaek","family":"Lim","sequence":"additional","affiliation":[{"name":"Korea University College of Medicine , , 73 Goryeodae-ro , ,","place":["Seoul, Republic of Korea, 02841"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hawon","family":"Lee","sequence":"additional","affiliation":[{"name":"Korea University College of Medicine , , 73 Goryeodae-ro , ,","place":["Seoul, Republic of Korea, 02841"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyunsu","family":"Bong","sequence":"additional","affiliation":[{"name":"Korea University College of Medicine , , 73 Goryeodae-ro , ,","place":["Seoul, Republic of Korea, 02841"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jitae","family":"Kim","sequence":"additional","affiliation":[{"name":"Korea University College of Medicine , , 73 Goryeodae-ro , ,","place":["Seoul, Republic of Korea, 02841"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5731-6186","authenticated-orcid":true,"given":"Minji","family":"Jeon","sequence":"additional","affiliation":[{"name":"Korea University College of Medicine , , 73 Goryeodae-ro , ,","place":["Seoul, Republic of Korea, 02841"]},{"name":"Korea University College of Medicine , , 161 Jeongreung-ro , ,","place":["Seoul, Republic of Korea, 02708"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"issue":"9","key":"2026101315075796500_cit1","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1001\/jama.2020.1166","article-title":"Estimated\nresearch and development\ninvestment needed to bring a new medicine to market, 2009-2018","volume":"323","author":"Wouters","year":"2020","journal-title":"JAMA"},{"issue":"7","key":"2026101315075796500_cit2","doi-asserted-by":"publisher","first-page":"3049","DOI":"10.1016\/j.apsb.2022.02.002","article-title":"Why 90% of clinical drug development\nfails and how to improve it?","volume":"12","author":"Sun","year":"2022","journal-title":"Acta Pharmaceutica\nSinica B"},{"key":"2026101315075796500_cit3","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s13321-017-0235-x","article-title":"Molecular\nde-novo design\nthrough deep reinforcement learning","volume":"9","author":"Olivecrona","year":"2017","journal-title":"Journal\nof cheminformatics"},{"issue":"2","key":"2026101315075796500_cit4","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1038\/nrd.2017.232","article-title":"Automating\ndrug discovery","volume":"17","author":"Schneider","year":"2018","journal-title":"Nat. Rev. Drug Discovery"},{"key":"2026101315075796500_cit5","doi-asserted-by":"publisher","first-page":"3408","DOI":"10.1038\/s41467-024-47613-w","article-title":"Prospective\nde novo drug design with deep interactome\nlearning","volume":"15","author":"Atz","year":"2024","journal-title":"Nat. Commun."},{"key":"2026101315075796500_cit6","unstructured":"Goodfellow, I. J.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; Bengio, Y.\n          Generative adversarial nets.  Advances in Neural Information Processing Systems, 2014; 27."},{"key":"2026101315075796500_cit7","unstructured":"Kingma, D. P.; Welling, M.\n          Auto-encoding\nVariational Bayes.  arXiv2013, abs\/1312.6114.10.48550\/arXiv.1312.6114."},{"key":"2026101315075796500_cit8","unstructured":"Ho, J.; Jain, A.; Abbeel, P.\n          Denoising diffusion probabilistic\nmodels.  Advances in Neural Information Processing\nSystems NeurIPS, 2020; Vol. 33, pp 6840\u20136851."},{"key":"2026101315075796500_cit9","unstructured":"Lipman, Y.; Chen, R. T. Q.; Ben-Hamu, H.; Nickel, M.; Le, M.\n          Flow matching for generative\nmodeling.  arXiv2022, abs\/2210.02747.10.48550\/arXiv.2210.02747."},{"key":"2026101315075796500_cit10","doi-asserted-by":"publisher","first-page":"1212","DOI":"10.1038\/s41598-023-50834-6","article-title":"MedGAN: optimized generative adversarial\nnetwork with graph convolutional networks for novel molecule design","volume":"14","author":"Macedo","year":"2024","journal-title":"Sci. Rep."},{"key":"2026101315075796500_cit11","doi-asserted-by":"publisher","first-page":"13352","DOI":"10.1039\/D4SC08736D","article-title":"Multi-objective\ndrug design with a scaffold-aware variational\nautoencoder","volume":"16","author":"Dong","year":"2025","journal-title":"Chem. 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