Machine Learning‐Assisted High‐Throughput Screening of Nanozymes for Ulcerative Colitis

Ulcerative colitis (UC) is a chronic gastrointestinal inflammatory disorder with rising prevalence. Due to the recurrent and difficult-to-treat nature of UC symptoms, current pharmacological treatments fail to meet patients' expectations. This study presents a machine learning-assisted high-throughput screening strategy to expedite the discovery of efficient nanozymes for UC treatment. Therapeutic requirements, including antioxidant property, acid stability, and zeta potential, are quantified and predicted by using a machine learning model. Non-quantifiable attributes, including intestinal barrier repair efficacy and biosafety, are assessed via high-throughput screening. Feature significance analysis, sure independence screening, and sparsifying operator symbolic regression reveal the high-dimensional structure-activity relationships between material features and therapeutic needs. SrDy