With the rapid advancements in computer technology and bioinformatics, the prediction of protein-ligand binding sites has become a central component of modern drug discovery and development. Traditional experimental methods are often constrained by long experimental cycles and high costs; therefore, the development of accurate and efficient computational methods is of paramount significance for conserving time and cost. This review comprehensively summarizes the methodological advancements and current applications in the field of screening for druggable protein target sites, systematically comparing the fundamental principles, advantages, and disadvantages of four main categories of methods: structure- and sequence-based methods, machine learning-based methods, binding site feature analysis methods, and druggability assessment methods. Subsequently, by integrating classic case studies, this paper elaborately discusses the technical support and theoretical guidance afforded by the screening of protein druggable target sites for drug discovery and drug repositioning. Finally, this paper thoroughly explores the current challenges inherent in the field of protein-ligand binding site prediction, with a particular focus on future technological trends, systematically elucidating the developmental prospects and potential applications of these predictive methods.

This review presents a comprehensive analysis of the electromagnetic shielding mechanisms, advanced synthesis techniques, and material optimization strategies for ceramic-based electromagnetic shielding materials. Meanwhile, this review discusses the research progress of traditional ceramics (such as oxides, carbides, borides, nitrides and ferrites) and emerging ceramics (such as polymer-derived ceramics, MAX phase ceramics and high-entropy ceramics). Furthermore, the review outlines future research directions in four key areas: microstructure engineering for high-efficiency electromagnetic shielding ceramics, advanced manufacturing technologies, multifunctional integration of shielding properties, and the development of artificial intelligence-driven design approaches for ceramic materials.

An AI from the University of Würzburg autonomously controlled a satellite in orbit for the first time, demonstrating the potential of intelligent, self-learning space systems. 

A study by researchers at Queen Mary University of London and University College London has found that humans have a form of remote touch, or the ability to sense objects without direct contact, a sense that some animals have.  

Human touch is typically understood as a proximal sense, limited to what we physically touch. However, recent findings in animal sensory systems have challenged this view. Certain shorebirds, such as sandpipers and plovers, use a form of “remote touch” to detect prey hidden beneath the sand (du Toit et al. 2020; de Fouw et al. 2016). Remote touch allows the detection of objects buried under granular materials through subtle mechanical cues transmitted through the medium, when a moving pressure is applied nearby. 

  The study in IEEE International Conference on Development and Learning (ICDL) investigated whether humans share a similar capability. Participants moved their fingers gently through sand to locate a hidden cube before physically touching it. Remarkably, the results revealed a comparable ability to that seen in shorebirds, despite humans lacking the specialized beak structures that enable this sense in birds. 

Development in artificial intelligence has paved the path for the integration of computational pathology in clinical workflow, improving the accuracy and reducing the workload for medical practitioners. In recent times, Foundation Models (FMs), trained on large-scale, unlabelled datasets, are considered more suitable than traditional models for diverse clinical tasks. In a review study published in the Chinese Medical Journal, researchers highlight the advancements in pathological FMs and discuss their application in precision oncology.