This project will develop methods to discover and design small molecules that control harmful microbes, prevent microbial-induced infrastructure deterioration, and detoxify fungal toxins in crops. It will draw on biology, mathematics, statistics, and computer science by leveraging artificial intelligence (AI) and machine learning (ML) tools to accelerate the identification of bioactive compounds. These efforts will lay the groundwork for future advances in human and animal health, the prevention of microbial corrosion in infrastructure, and enhanced food safety. Spanning the EPSCoR jurisdictions of South Dakota and Nevada, the collaborative effort will strengthen regional economies and national well-being. South Dakota State University (SDSU), as the lead institution, will partner with South Dakota Mines (SDM) and the University of Nevada, Las Vegas (UNLV) to broaden STEM access and involve students across a broad spectrum of experiences. This use-inspired project will develop a generative AI/ML platform for the rapid screening and optimization of small molecules targeting key microbial proteins. It will pursue three thrusts: (1) activators inducing bacterial cell death; (2) inhibitors of conserved biofilm maintenance proteins to mitigate biofouling; and (3) neutralizers of fungal toxins. The methodology integrates computational molecular modeling, high-throughput virtual screening, and experimental validation in bacterial and fungal assays. Infrastructure enhancements inc