Docking Simulation and Artificial-Intelligence-Assisted Molecularly Imprinted Polymer Electrochemical Sensor for the Detection of Cancer Biomarkers
- 1Gachon University , , ,
Abstract
Cancer remains one of the leading life-threatening diseases worldwide, and therefore, early stage detection of cancer biomarkers is vital for effective diagnosis and treatment planning. Conventional detection techniques primarily rely on biological recognition elements, including enzymes, antibodies, aptamers, DNA, RNA; however, these elements are often prone to denaturation and have limited stability under extreme environmental conditions and long-term storage. To overcome these obstacles, molecularly imprinted polymers have emerged as a promising alternative to traditional biorecognition elements because of the ease of synthesis, low expenditure, and relative stability. In addition, MIP-based electrochemical sensors prove their applicability in detecting various disease biomarkers in general and cancer biomarkers in particular. This study presents an overview of MIPs and MIP-based electrochemical sensors, with a particular focus on their application in cancer biomarkers detection. Representative studies are discussed in detail to illustrate the structure and efficacy of these systems. In particular, strategies for improving imprinting factors of the molecular docking and artificial-intelligence-assisted MIP-electrochemical sensors up to 6.45- or 3-fold higher compared to the conventional approaches are considered in detail. Finally, current limitations and challenges, as well as future perspectives and potential solutions, are critically discussed and proposed.
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BibTeX
@article{le2026docking,
title = {{Docking Simulation and Artificial-Intelligence-Assisted Molecularly Imprinted Polymer Electrochemical Sensor for the Detection of Cancer Biomarkers}},
author = {Phan Gia Le and Sungbo Cho},
journal = {ACS Omega},
year = {2026},
month = nov,
doi = {10.1021/acsomega.6c07634},
url = {https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c07634/5466186/Docking-Simulation-and-Artificial-Intelligence},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Docking Simulation and Artificial-Intelligence-Assisted Molecularly Imprinted Polymer Electrochemical Sensor for the Detection of Cancer Biomarkers
AU - Phan Gia Le
AU - Sungbo Cho
PY - 2026
DA - 2026/11/07
JO - ACS Omega
DO - 10.1021/acsomega.6c07634
UR - https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c07634/5466186/Docking-Simulation-and-Artificial-Intelligence
AB - Cancer remains one of the leading life-threatening diseases worldwide, and therefore, early stage detection of cancer biomarkers is vital for effective diagnosis and treatment planning. Conventional detection techniques primarily rely on biological recognition elements, including enzymes, antibodies, aptamers, DNA, RNA; however, these elements are often prone to denaturation and have limited stability under extreme environmental conditions and long-term storage. To overcome these obstacles, molecularly imprinted polymers have emerged as a promising alternative to traditional biorecognition elements because of the ease of synthesis, low expenditure, and relative stability. In addition, MIP-based electrochemical sensors prove their applicability in detecting various disease biomarkers in general and cancer biomarkers in particular. This study presents an overview of MIPs and MIP-based electrochemical sensors, with a particular focus on their application in cancer biomarkers detection. Representative studies are discussed in detail to illustrate the structure and efficacy of these systems. In particular, strategies for improving imprinting factors of the molecular docking and artificial-intelligence-assisted MIP-electrochemical sensors up to 6.45- or 3-fold higher compared to the conventional approaches are considered in detail. Finally, current limitations and challenges, as well as future perspectives and potential solutions, are critically discussed and proposed.
SN - 2470-1343
ER -