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.
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Dr. Sapna Jain Dabade (SAGE University) specializes in AI, computational drug design, sustainability, innovation and interdisciplinary research.Dr. Dheeraj Mandloi (IET-DAVV) is an expert in applied chemistry and a mentor in the field of research. Prof. (Dr.) Amrit Lal Bajaj (DAVV) is a distinguished chemist, mentor, and academic leader.
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Taschenbuch. Zustand: Neu. Computer-Aided Antifungal Drug Design | Potential N-Myristoyltransferase (NMT) Inhibitors of Candida albicans | Sapna Jain Dabade (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630121810 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 135958054
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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. 188 pp. Englisch. Bestandsnummer des Verkäufers 9786630121810
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