Researchers publishing in Computational Biomedicine have developed a novel artificial intelligence framework, MIGDTA, that integrates graph neural networks and multi-source biological information to enhance drug–target affinity prediction. The study highlights how combining molecular structure, biological descriptors, and interaction networks could accelerate drug discovery and repositioning.

A new study outlines a comprehensive framework leveraging AI and big data to combat illegal plant trade, addressing global biodiversity threats with technological innovation.

Single-nucleus RNA-seq atlas of equine skeletal muscle reveals donkey and horse distinct cell types, with species-specific age-related shifts in muscle fiber composition. The conserved upregulation pattern of LDHA reveals the critical role of propanoate metabolism in energy supply for equine locomotion. Cross-species transcriptomic comparison delineates a highly conserved atlas of skeletal muscle cell types.

This review highlights spider silk as a next-generation eco-smart biomaterial with broad potential across dermatology, cosmetics, aerospace, textiles, and electronics. Owing to its exceptional strength, elasticity, biodegradability, biocompatibility, and low immunogenicity, spider silk is increasingly explored for wound healing, tissue regeneration, drug delivery, scar repair, and smart dermatologic theranostics. In cosmetics, its amino acid-rich structure supports skin hydration, elasticity, and barrier repair. Beyond healthcare, its lightweight strength, thermal stability, optical clarity, and flexibility enable applications in advanced composites, biodegradable sensors, and wearable devices, making spider silk a promising sustainable material for future industries.