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

Abstract

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. SrDy₂O₄ with high stability, low toxicity, targeting ability, and reactive oxygen species (ROS) scavenging capability is identified, which reduces ROS production, lowers cytochrome C levels in cytoplasm, and inhibits apoptosis in intestinal epithelial cells by stabilizing the mitochondrial membrane potential. Mice treated with SrDy₂O₄ show improvements in colon length and body weight compared with dextran sodium sulfate salt-treated model group. Transcriptomic and 16S rRNA sequencing analyses show that SrDy₂O₄ boosts beneficial gut bacteria, and decreases pathogenic bacteria, thereby effectively restoring gut microbiota balance. Moreover, SrDy₂O₄ offers the advantage of X-ray imaging without side effects.