Artificial Intelligence in High-Performance Liquid Chromatography: Transforming Method Development, Optimization, and Data Analysis- A Comprehensive Review

High-Performance Liquid Chromatography (HPLC) is a critical analytical technique for separating, identifying, and quantifying compounds, but traditional gradient method development is resource-intensive and time-consuming. This review explores the integration of artificial intelligence (AI) to enhance HPLC method development and data analysis. Systematic literature from various online sources highlights how machine learning algorithms and artificial neural networks optimize chromatographic conditions by predicting effective combinations of mobile phase, flow rate, pH, and temperature, reducing experimental trials. AI-driven automation, including self-driving laboratories and adaptive optimization algorithms like genetic algorithms, enables efficient method screening and real-time refinement with minimal human intervention. In data analysis, AI improves peak identification, deconvolution, outlier detection, and quality control, enhancing reliability of quantitative results. Case studies from Pfizer and Merck demonstrate reduced development timelines and faster regulatory approvals through AI adoption. Despite challenges in data quality, workflow integration, and regulatory acceptance, AI presents a transformative approach for developing robust, efficient, and reliable HPLC methods across pharmaceutical, environmental, and food safety applications.