Minisymposium Presentation

Phosphorus is an essential element for life and a critical component of agricultural fertilizers, yet its sustainable management remains a pressing global challenge. Excess phosphorus runoff contributes to environmental pollution, while limited high-quality phosphate rock reserves raise concerns about future availability. This study explores the intersection of AI and molecular modeling to advance phosphorus sustainability. By leveraging molecular dynamics simulations and AI-driven predictive modeling, we investigate the interactions between phosphorus-binding proteins and various phosphorus species to enhance selective capture and recycling strategies. Machine learning algorithms are applied to predict modular peptide sequences with high binding affinity for phosphorus, facilitating the design of novel biomimetic materials for phosphorus recovery. Additionally, we utilize AI-powered data integration to analyze large-scale phosphorus-related datasets, enabling more efficient resource utilization and policy development. This interdisciplinary approach has the potential to revolutionize phosphorus management by improving recovery efficiency, reducing environmental impact, and ensuring long-term sustainability.