Artificial Intelligence application for diagnosis biomarkers, medicine designing and Chemotherapy for Breast Cancer (BC) using PEG linkage

Background: Chemotherapy is a fundamental and rapidly acting modality for cancer treatment that employs specific anticancer agents such as cyclophosphamide, 5-fluorouracil, and methotrexate. It is administered either with curative intent, typically through defined drug combinations, or with palliative intent, aiming to prolong survival and alleviate symptoms. Aim: This work highlights the transformative synergy between artificial intelligence (AI) and quantum biology in advancing the detection and treatment of breast cancer. Recent clinical research in chemotherapy underscores the expanding role of AI-driven deep learning (DL) frameworks and machine learning (ML) applications in OMIC-based precision diagnostics, rational drug design, and personalized therapeutic strategies enabled through biomarker discovery. The integration of intelligent medical engineering with quantum biological principles and deep learning methodologies offers significant advancements in targeted drug delivery, minimizes the adverse effects and toxicity associated with chemotherapeutic agents, and enables robust real-time monitoring of treatment responses. Collectively, these innovations demonstrate substantial potential to enhance therapeutic efficacy and improve clinical outcomes in breast cancer (BC) management. Methodology: We simulated a series of anti-breast-cancer agents including cyclophosphamide, 5-fluorouracil, and methotrexate combined via a polyethylene glycol (PEG) linker using QM/MM methods. This approach aims to improve therapeutic effectiveness while reducing side effects, guided by deep learning and AI-based optimization strategies. Finally, this study provides an overview of current trends in AI-based healthcare nanotechnologies and concludes that the integration of AI into healthcare offers significant benefits, paving the way toward a more advanced and effective future in medicine. Results: PEG conjugation in CP–[PEG(n)]–5-FU conjugates represents a strategy to extend circulation time and reduce immunogenicity by increasing hydrodynamic size and shielding epitopes from immune surveillance .The amino acids from the ligand {CP–[PEG(n)]–5-FU (n = 2–18)} that interact with 1JNX are Cys1697, Arg1699, and Asp1739 in the H–M interaction, Asp1692 and Cys1692 in the V–M interaction, Glu1698 and Val1740 in the V–S interaction, and finally only one residue (Glu1698) in the H–S interaction. These residues are also completely located within the active site of 1JNX, which represents the crystal structure of the BRCT region associated with BC. Conclusions: Up to now, biotechnology in medical Artificial intelligence (AI) has immense potential for various applications in the pharmaceutical industry. AI enables the design of novel drugs for delivery to tissue and cell targets based on the discovery of specific chemical structures. A subfield of AI known as machine learning is commonly used in disease analysis and detection. Obviously, the results of predicting experimental help improve the precision of the testing output, significantly. Based on machine learning analysis, the systems {CP–[PEG(n)]–5-FU (n = 30–35)} were identified as the most favorable candidates for these simulations.