Computer-Aided Antifungal Drug Design: Potential N-Myristoyltransferase (NMT) Inhibitors of Candida albicans

The book presents an integrated computational approach for antifungal drug discovery targeting N-myristoyltransferase (NMT) in Candida albicans, a promising therapeutic target against antifungal resistance. It highlights the application of computer-aided drug design (CADD) techniques, particularly QSAR modeling using Genetic Algorithm-Stepwise Multiple Linear Regression (GA-SMLR) and molecular docking studies, to identify and optimize novel NMT inhibitors. The study evaluates bisamidine, coumarin, and triazole derivatives, revealing key molecular descriptors, structural features, and binding interactions responsible for antifungal activity. Rigorous internal and external validation ensures the statistical robustness and predictive reliability of the QSAR models. By combining structure-activity relationships with protein-ligand interaction analysis, the book provides valuable insights into rational drug design, lead optimization, and antifungal therapeutic development. It serves as a practical resource for researchers and students in medicinal chemistry, pharmaceutical sciences, computational biology, and antifungal drug discovery.