Abstract
Artificial intelligence (AI) is transforming how routine molecular techniques are interpreted, moving beyond traditional statistics such as band intensity, protein concentration, and visual scoring. This review examines how AI enhances the analytical capacity of four widely used methods: Western blotting, ELISA, Immunostaining, and Immunoprecipitation-Mass Spectrometry (IP-MS). Unlike conventional approaches that analyze each measurement in isolation, AI detects hidden relationships across multiple signals, integrates temporal and spatial information, and reconstructs coherent biological pathways from fragmented experimental data. It extracts detailed features from Western blot images, identifies interaction patterns in ELISA datasets, quantifies protein localization in stained tissues, and builds reliable protein-protein interaction networks from IP-MS experiments. Beyond data analysis, AI guides experimental design, generates testable novel hypotheses, and facilitates translation of molecular findings into clinical applications. Despite challenges related to data quality, model interpretability, and validation, AI is increasingly positioned as a complementary tool that works alongside human expertise. By converting routine laboratory assays into innovative, comprehensive discoveries, AI accelerates mechanistic understanding and bridges the gap between basic molecular biology and precision medicine. This review provides an informative overview of these applications and discusses future directions and limitations for AI-driven molecular research.