Corrigendum to: miRNA in Diagnosis and Therapeutics of Tuberculosis: Importance in Latent and Brain Associated Pathologies

Artificial intelligence and in silico methods speed up cancer drug discovery, transforming how new treatments are developed. Artificial intelligence, along with in silico methods, allows for quick investigation of large biological datasets, helping identify potential drug targets with remarkable speed and accuracy. Machine learning models help us understand how molecules ...read more

Computer-aided drug design (CADD) and artificial intelligence (AI) are fundamentally reshaping drug discovery pipelines by significantly enhancing efficiency in molecular screening, rational drug design, and natural product development. In the field of molecular screening, the integration of virtual high-throughput screening with advanced AI models enables rapid analysis of million-compound libraries, ...read more

Machine learning and deep learning are transforming biomedical diagnosis by allowing for quicker and more accurate disease detection, ultimately enhancing patient care. Using advanced algorithms and extensive medical data, these approaches uncover patterns that may not be visible to humans, resulting in better detection of conditions such as cancer and ...read more

The concept of allostery, where ligand binding at a site distinct from the active or orthosteric site modulates protein function, has emerged as a transformative paradigm in modern drug discovery. Unlike conventional orthosteric drugs that often face limitations in selectivity and off-target effects, allosteric modulators offer a promising alternative. Their ...read more