Prediction of newly synthesized heparin mimic's effects as heparanase inhibitor in cancer treatments via variational quantum neural networks

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dc.authoridMemis, Samet/0000-0002-0958-5872
dc.authoridSener, Ramazan/0000-0001-6108-8673
dc.authoridSEVER, Meryem Ruveyda/0000-0001-9271-1528
dc.contributor.authorKocabay, Samet
dc.contributor.authorAcar, Erdi
dc.contributor.authorMemis, Samet
dc.contributor.authorTaskin, Irmak Icen
dc.contributor.authorSever, Meryem Ruveyda
dc.contributor.authorSener, Ramazan
dc.date.accessioned2025-05-29T02:58:01Z
dc.date.available2025-05-29T02:58:01Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractCancer remains a leading global cause of death, primarily driven by the uncontrolled proliferation of abnormal cells. Malignant tumors, such as carcinomas, originate from unchecked epithelial cell growth and produce growth factors like FGF and VEGF, which promote angiogenesis and tumor progression through heparanasemediated degradation of heparan-sulfate proteoglycans. Chitosan and its derivatives have shown promise in inhibiting tumor growth and metastasis. This study aims to investigate newly synthesized sulfated chitosan oligomers as heparin mimics to inhibit heparanase, evaluating their cytotoxic effects on SH-SY5Y, HCT116, A549, and MDA-MB-231 cancer cell lines. Moreover, it seeks to leverage a variational quantum neural network (VQNN) to predict and validate cytotoxicity outcomes, integrating quantum computing methods into evaluating novel anticancer compounds. The VQNN algorithm was applied to analyze the anticancer effects of sulfated chitosan oligomers. Cytotoxicity data from wet lab experiments validated the model's predictive performance. The VQNN model demonstrated strong predictive capabilities in evaluating anticancer compounds. Specifically, it achieved a mean absolute error (MAE) of 6.5844, indicating a similar trend to the experimental results. Additionally, the model obtained an R2 value of 0.6020, reflecting a moderate level of correlation between predicted and observed outcomes. The results underscore the potential of integrating quantum-based machine learning models into cancer research. The VQNN effectively predicted experimental outcomes, showcasing its utility in assessing novel anticancer compounds. This approach could speed up drug discovery by streamlining the identification and optimization of therapeutic candidates. Furthermore, the findings support the ongoing development of quantum computing techniques for tackling complex biological challenges, contributing to innovative cancer treatment strategies that target tumor growth and angiogenesis.
dc.identifier.doi10.1016/j.compbiolchem.2025.108476
dc.identifier.issn1476-9271
dc.identifier.issn1476-928X
dc.identifier.pmid40267546
dc.identifier.scopus2-s2.0-105003137574
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.compbiolchem.2025.108476
dc.identifier.urihttps://hdl.handle.net/20.500.12428/30243
dc.identifier.volume118
dc.identifier.wosWOS:001478463400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofComputational Biology and Chemistry
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250529
dc.subjectCancer
dc.subjectHeparanase
dc.subjectChitosan
dc.subjectQuantum Computing
dc.subjectVariational Quantum Neural Networks
dc.titlePrediction of newly synthesized heparin mimic's effects as heparanase inhibitor in cancer treatments via variational quantum neural networks
dc.typeArticle

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