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However, existing computational models face limitations in sequence representation and class imbalance. To address these challenges, we propose UACD-ACPs, a unified fusion-driven framework that integrates a diffusion-inspired noise-conditioned classifier for ACP prediction and a diffusion-based peptide generation module with cancer-type-aware organization for targeted downstream screening. The classification module integrates ProtBERT-based semantic embeddings with physicochemical descriptors via the Multiscale Embedding Compression Strategy (MECS) and a diffusion-inspired noise-conditioned encoder, substantially enhancing predictive robustness and accuracy, particularly under challenging imbalanced multi-class settings. In the generative pipeline, we introduce a denoising diffusion-based generative framework augmented by two novel fusion modules: the Bitemporal Fusion Module (BFM) and the Temporal Feature Attention Module (TFAM). These modules perform multi-scale temporal and semantic fusion to promote the generation of structurally coherent and functionally relevant peptide candidates. Experimental results demonstrate that UACD-ACPs outperforms state-of-the-art methods in terms of accuracy, F1-score, and AUC-ROC. The generated peptides exhibit favorable physicochemical properties, diverse secondary structures, and strong structural stability, as validated by molecular dynamics simulations and membrane-binding analyses. Overall, this study highlights the potential of fusion-driven diffusion-based frameworks for alleviating class imbalance and data heterogeneity in anticancer peptide modeling, paving the way for scalable and biologically grounded ACP discovery.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1014098","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T20:02:05Z","timestamp":1774555325000},"page":"e1014098","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing anticancer peptide discovery: A fusion-centric framework with conditional diffusion for prediction and generation"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4703-1333","authenticated-orcid":true,"given":"Binyu","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8868-8350","authenticated-orcid":true,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhihua","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Prayag","family":"Tiwari","sequence":"additional","affiliation":[]},{"given":"Quan","family":"Zou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2911-7643","authenticated-orcid":true,"given":"Yijie","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Xiaoyi","family":"Guo","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"issue":"1","key":"pcbi.1014098.ref001","first-page":"17","article-title":"Cancer statistics, 2023","volume":"73","author":"RL Siegel","year":"2023","journal-title":"CA: a cancer journal for clinicians"},{"issue":"6","key":"pcbi.1014098.ref002","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1039\/c2mt20054f","article-title":"Cucurbit[7]uril encapsulated cisplatin overcomes cisplatin resistance via a pharmacokinetic effect","volume":"4","author":"JA Plumb","year":"2012","journal-title":"Metallomics"},{"issue":"3","key":"pcbi.1014098.ref003","doi-asserted-by":"crossref","first-page":"678","DOI":"10.3892\/ijo.2020.5099","article-title":"Anticancer peptide: Physicochemical property, functional aspect and trend in clinical application (Review)","volume":"57","author":"W Chiangjong","year":"2020","journal-title":"Int J Oncol"},{"key":"pcbi.1014098.ref004","doi-asserted-by":"crossref","first-page":"294","DOI":"10.3389\/fmicb.2013.00294","article-title":"From antimicrobial to anticancer peptides. 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