Work

Docking Simulation and Artificial-Intelligence-Assisted Molecularly Imprinted Polymer Electrochemical Sensor for the Detection of Cancer Biomarkers

Year: 2026

Type: article

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.

Source: ACS Omega

DOI: 10.1021/acsomega.6c07634

Authors: Phan Gia Le, Sungbo Cho

Institution: Gachon University

Language: English

Topic: Analytical chemistry methods development

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,935