The field of nanomaterial-protein interactions and anomaly detection is rapidly advancing, driven by the development of large-scale datasets and innovative machine learning models. Researchers are leveraging these advances to improve the accuracy and efficiency of defect detection and materials discovery. A key direction in this field is the use of multimodal representation learning and latent diffusion models to predict nanomaterial-protein affinities and detect anomalies in complex systems. These approaches have shown significant promise in overcoming the limitations of traditional methods and achieving state-of-the-art performance in various applications. Notably, the development of class-agnostic frameworks for long-tailed online anomaly detection has enabled the detection of defects in realistic settings without requiring class labels. Overall, the field is moving towards more accurate, efficient, and generalizable models that can handle complex datasets and real-world applications. Some noteworthy papers include: NanoPro-3M and NanoProFormer, which demonstrate strong generalization and handling of missing features and unseen nanomaterials or proteins. ExDD, which achieves superior performance in surface defect detection via diffusion synthesis. RoadFusion, which sets a new state-of-the-art in pavement defect detection using latent diffusion models and dual-path feature adaptation.