We developed the Automated Network Optimization (ANO) framework to enhance DNN predictions by systematically selecting optimal features and tuning hyperparameters through In this stage, additional chemical features, denoted as 𝑓𝑒𝑎 𝑘 are selected from a pool of 𝑗 candidate features. The objective is to identify the set of 𝑘 features that maximizes DNN's predictive performance while minimizing a penalty term. The optimization is expressed as: